Tag: Containerized Solutions

AI-200, azure, Microsoft Certification August 15, 2026August 15, 2026

Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files (AI-200 Exam Prep)

This post is a part of the AI-200: Developing AI Cloud Solutions on Azure  Exam Prep Hub.
This topic falls under these sections:
Develop containerized solutions on Azure (20–25%)
   --> Implement container-orchestrated solutions
      --> Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Azure Kubernetes Service (AKS) provides a managed Kubernetes environment for deploying and operating containerized applications. For AI solutions, AKS can be particularly useful when applications require container orchestration, multiple cooperating services, custom networking, persistent workloads, or more control over Kubernetes configuration.

One of the fundamental skills for working with AKS is the ability to define and deploy applications by using Kubernetes manifest files.

A Kubernetes manifest is a declarative configuration file, commonly written in YAML, that describes the desired state of Kubernetes resources. Rather than manually creating each resource with individual commands, developers can define the application’s resources in one or more manifest files and use kubectl to apply those definitions to an AKS cluster.

The AI-200 exam expects you to understand how these manifests are structured, how they are deployed, how Kubernetes resources work together, and how to manage and troubleshoot the resulting application.

1. Understanding Kubernetes Manifest Files

A Kubernetes manifest describes one or more Kubernetes resources.

A typical manifest specifies information such as:

The resource type
The resource name
The container image
The number of replicas
Container ports
Environment variables
Resource requests and limits
Configuration references
Secrets
Health probes
Labels and selectors
Service configuration
Storage requirements

A manifest is declarative.

That distinction is important.

Instead of telling Kubernetes:

Start three containers, then create a network endpoint, then connect the endpoint to those containers.

you describe the desired state:

I want a Deployment with three replicas and a Service that selects those replicas.

Kubernetes controllers continuously work toward making the actual state of the cluster match the desired state defined by the manifests.

2. YAML Manifest Structure

A basic Kubernetes manifest typically contains:

			
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-api
  template:
    metadata:
      labels:
        app: ai-api
    spec:
      containers:
        - name: ai-api
          image: myregistry.azurecr.io/ai-api:v1
          ports:
            - containerPort: 8080

		

The major sections are:

Property	Purpose
`apiVersion`	Specifies the Kubernetes API version used by the resource
`kind`	Specifies the type of Kubernetes resource
`metadata`	Provides identifying information such as name and labels
`spec`	Defines the desired configuration of the resource

For the exam, be comfortable recognizing the relationship between these sections.

3. The `apiVersion` Property

apiVersion identifies the API group and version used to create the resource.

For example:

apiVersion: apps/v1

is commonly used for a Deployment.

A Service generally uses:

apiVersion: v1

The API version matters because Kubernetes resources belong to different API groups and versions.

For example:

			
apiVersion: apps/v1
kind: Deployment

is different from:

			
apiVersion: v1
kind: Service

The apiVersion must be appropriate for the resource being defined.

4. The `kind` Property

The kind property identifies the Kubernetes resource being created.

Common resources include:

Deployment
Service
Pod
ConfigMap
Secret
StatefulSet
Job
CronJob
Ingress
HorizontalPodAutoscaler

For AI-200, pay particular attention to Deployment and Service, as these are fundamental to deploying and exposing applications.

5. Kubernetes Deployments

A Deployment manages a set of replicated Pods.

For example:

			
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-api
  template:
    metadata:
      labels:
        app: ai-api
    spec:
      containers:
        - name: ai-api
          image: myregistry.azurecr.io/ai-api:v1
          ports:
            - containerPort: 8080

		

The important relationship is:

Deployment → ReplicaSets → Pods

The Deployment controller creates and manages a ReplicaSet, which in turn maintains the requested number of Pods.

If:

replicas: 3

is specified, Kubernetes attempts to maintain three Pods matching the Deployment’s selector.

If a Pod fails, Kubernetes can create a replacement.

6. Labels and Selectors

Labels are extremely important in Kubernetes.

A label identifies or categorizes a resource:

			
labels:
  app: ai-api

A selector determines which resources should be associated with another resource.

For example:

			
selector:
  matchLabels:
    app: ai-api

The Deployment’s selector must correspond to labels on its Pod template.

A Service can then use the same label:

			
selector:
  app: ai-api

This allows the Service to route traffic to the appropriate Pods.

Exam Tip

A common exam scenario presents a Deployment and Service that aren’t communicating.

Check the Service selector and the Pod labels.

For example:

			
# Pod
labels:
  app: ai-api

and:

			
# Service
selector:
  app: ai-api

match.

But:

			
selector:
  app: api

does not.

A mismatch can result in a Service with no appropriate endpoints.

7. Container Images

A Deployment specifies the image that Kubernetes should run:

			
containers:
  - name: ai-api
    image: myregistry.azurecr.io/ai-api:v1

For Azure-based applications, the image may be stored in Azure Container Registry (ACR).

The image reference generally contains:

<registry>/<repository>:<tag>

For example:

contosoregistry.azurecr.io/inference-api:2.1

The tag identifies the particular version of the image.

Best Practice

Avoid relying on ambiguous tags such as:

latest

for production deployments when deterministic versioning is important.

Using an explicit version such as:

inference-api:2.1.4

makes deployments easier to reproduce and troubleshoot.

8. Connecting AKS to Azure Container Registry

An AKS application frequently pulls its container images from ACR.

The AKS cluster must have appropriate permissions to pull the image.

For example, Azure CLI can be used to attach an ACR to an AKS cluster:

			
az aks update \
  --resource-group myResourceGroup \
  --name myAKSCluster \
  --attach-acr myRegistry

The exact identity and authorization configuration can vary depending on how the AKS cluster is configured.

The important concept is:

AKS must be authorized to pull the private container image.

If the image cannot be pulled, Pods may enter states such as:

ImagePullBackOff

or:

ErrImagePull

9. Exposing an Application with a Service

A Pod’s IP address is generally not intended to be the stable endpoint for an application.

A Kubernetes Service provides a stable network abstraction for accessing a set of Pods.

Example:

			
apiVersion: v1
kind: Service
metadata:
  name: ai-api
spec:
  selector:
    app: ai-api
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

		

Here:

port: 80 is the Service port.
targetPort: 8080 is the container/application port.
selector: app: ai-api identifies the Pods receiving traffic.
type: LoadBalancer requests an externally accessible load-balancing endpoint through the cloud provider integration.

10. Service Types

The most important Service types to recognize are:

ClusterIP

type: ClusterIP

This is the default Service type.

It provides an internal cluster endpoint.

Use it when the application should be reachable from within the Kubernetes cluster but doesn’t need to be directly exposed externally.

NodePort

type: NodePort

Exposes the Service through a port on each node.

It is useful in certain scenarios but is generally less convenient than higher-level ingress or load-balancing approaches for production web applications.

LoadBalancer

type: LoadBalancer

Requests an external load balancer from the cloud provider.

In AKS, this can provide an externally reachable IP address for the application.

A newly created LoadBalancer Service may initially show:

EXTERNAL-IP   <pending>

until the Azure networking resources are provisioned.

11. Deploying a Manifest to AKS

Once kubectl is configured to communicate with the AKS cluster, the primary command for applying a manifest is:

kubectl apply -f deployment.yaml

For example:

kubectl apply -f ai-api.yaml

kubectl apply is an important command because it applies the desired configuration described by the manifest to the cluster.

Microsoft’s AKS documentation uses this pattern for deploying applications from YAML manifests.

You can also apply a directory:

kubectl apply -f ./manifests/

This is useful when an application consists of multiple YAML files.

12. Applying Multiple Resources in One File

A YAML file can contain multiple Kubernetes resources.

The resources are separated using:

---

For example:

			
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-api
spec:
  ...
---
apiVersion: v1
kind: Service
metadata:
  name: ai-api
spec:
  ...

		

This allows the Deployment and Service to be maintained together.

An AKS application can therefore be deployed using a single command:

kubectl apply -f ai-api.yaml

The manifest can create multiple Kubernetes objects.

13. Connecting `kubectl` to AKS

Before deploying an application, kubectl must be configured to communicate with the correct AKS cluster.

A common command is:

			
az aks get-credentials \
  --resource-group myResourceGroup \
  --name myAKSCluster

This configures the local Kubernetes client with credentials and cluster information.

You can then verify connectivity:

kubectl get nodes

If the connection is successful, the cluster’s nodes should be displayed.

Exam Tip

Know the distinction:

az aks ...

is used to manage/interact with the Azure AKS resource.

kubectl ...

is used to interact with Kubernetes resources running in the cluster.

14. Namespaces

Namespaces provide logical isolation within a Kubernetes cluster.

A manifest can specify a namespace:

			
metadata:
  name: ai-api
  namespace: production

Alternatively, the namespace can be supplied when using kubectl:

kubectl apply -f ai-api.yaml -n production

You can view resources in a namespace with:

kubectl get pods -n production

Namespaces are useful for separating environments or application components.

For example:

			
development
testing
production

can exist within the same cluster.

15. Environment Variables

Container applications often require configuration through environment variables.

A manifest can specify them directly:

			
env:
  - name: MODEL_NAME
    value: "my-model"
  - name: LOG_LEVEL
    value: "Information"

		

However, application configuration should generally be separated from the container image.

Kubernetes provides ConfigMaps for non-sensitive configuration and Secrets for sensitive information.

16. ConfigMaps

A ConfigMap stores non-sensitive configuration data.

Example:

			
apiVersion: v1
kind: ConfigMap
metadata:
  name: ai-config
data:
  MODEL_NAME: "my-model"
  LOG_LEVEL: "Information"

		

A Deployment can consume the values:

			
envFrom:
  - configMapRef:
      name: ai-config

This makes it possible to change configuration without rebuilding the container image.

17. Kubernetes Secrets

Sensitive information should not normally be hard-coded into a Deployment manifest.

Kubernetes Secrets can be used to store sensitive configuration such as:

Passwords
API keys
Connection strings
Certificates

For example:

			
env:
  - name: API_KEY
    valueFrom:
      secretKeyRef:
        name: ai-secrets
        key: api-key

		

For production Azure applications, you should also understand Azure-native approaches for managing secrets, such as Azure Key Vault and workload identity, rather than treating a Kubernetes Secret as equivalent to a fully managed secret-management solution.

18. Resource Requests and Limits

Containers can specify CPU and memory requests and limits.

For example:

			
resources:
  requests:
    cpu: "250m"
    memory: "256Mi"
  limits:
    cpu: "1"
    memory: "512Mi"

		

Requests

A request indicates the resources needed for scheduling.

Kubernetes uses requests when determining where a Pod can run.

Limits

A limit establishes the maximum resource usage permitted for the container.

For AI workloads, resource specifications can be particularly important because inference workloads can consume substantial CPU or memory.

19. Health Probes

Kubernetes supports health probes that help determine application health.

Three important probe concepts are:

Startup probe

Determines whether an application has successfully started.

This is particularly useful for applications that take a long time to initialize.

Readiness probe

Determines whether the application is ready to receive traffic.

If a container isn’t ready, Kubernetes can prevent traffic from being sent to it through a Service.

Liveness probe

Determines whether the container is still functioning correctly.

If the liveness probe repeatedly fails, Kubernetes can restart the container.

Example:

			
readinessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

		

Exam Distinction

Remember:

Readiness = Should this Pod receive traffic?

Liveness = Is this container still healthy enough to keep running?

Startup = Has this application finished starting?

20. Updating an Application

One of the major benefits of declarative manifests is that you can modify the desired state and apply it again.

For example, changing:

image: myregistry.azurecr.io/ai-api:v1

to:

image: myregistry.azurecr.io/ai-api:v2

and running:

kubectl apply -f ai-api.yaml

causes Kubernetes to reconcile the Deployment with the new desired configuration.

A Deployment can perform a rolling update, gradually replacing existing Pods with Pods running the new version.

This reduces application downtime compared with manually deleting all existing Pods.

21. Checking Deployment Status

After deploying a manifest, use:

kubectl get deployments

For more detailed information:

kubectl describe deployment ai-api

To inspect Pods:

kubectl get pods

To obtain additional information:

kubectl get pods -o wide

You can also watch changes:

kubectl get pods --watch

These commands are important for verifying whether the application has successfully transitioned to its desired state.

22. Viewing Application Logs

If a container is running but the application isn’t behaving correctly, inspect its logs:

kubectl logs <pod-name>

If a Pod contains multiple containers:

kubectl logs <pod-name> -c <container-name>

Logs are often the first place to look for application-level failures.

23. Using `kubectl describe`

When Kubernetes reports an unexpected condition, kubectl describe provides useful diagnostic information.

For example:

kubectl describe pod <pod-name>

This can reveal:

Scheduling problems
Container image errors
Failed probes
Mount failures
Events
Resource issues

For a Service:

kubectl describe service ai-api

can help identify configuration problems.

24. Common Deployment Problems

Several problems are particularly useful to recognize for the exam.

`ImagePullBackOff`

Usually indicates that Kubernetes cannot successfully pull the specified image.

Potential causes include:

Incorrect image name
Incorrect tag
Image doesn’t exist
Authentication/authorization failure
Registry connectivity issue

`CrashLoopBackOff`

Indicates that a container repeatedly starts and then fails.

Potential causes include:

Application startup failure
Invalid configuration
Missing environment variables
Application exception
Dependency failure

Start troubleshooting with:

kubectl logs <pod-name>

and:

kubectl describe pod <pod-name>

Pod stuck in `Pending`

A Pod may remain Pending because:

No node has sufficient resources
Node selectors don’t match available nodes
A required volume cannot be provisioned
Scheduling constraints cannot be satisfied

Inspect:

kubectl describe pod <pod-name>

for scheduling events.

Service has no endpoints

If a Service exists but isn’t routing traffic, check:

Service selector
Pod labels
Pod readiness
Service and container ports

For example:

			
selector:
  app: ai-api

must correspond to:

			
labels:
  app: ai-api

25. Managing Applications Declaratively

The major conceptual advantage of manifests is that they allow infrastructure and application configuration to be represented as code.

Instead of manually configuring a production environment, the desired configuration can be stored in source control.

For example:

			
/manifests
    namespace.yaml
    configmap.yaml
    deployment.yaml
    service.yaml

		

A deployment pipeline can then apply these manifests to an AKS cluster.

This provides:

Repeatability
Version control
Change tracking
Easier rollback
Consistent environments
Automation
Infrastructure-as-code characteristics

26. Manifest Files and CI/CD

Manifest files fit naturally into CI/CD processes.

A typical workflow might look like:

			
Developer commits code
        ↓
Build container image
        ↓
Push image to ACR
        ↓
Update Kubernetes manifest
        ↓
CI/CD pipeline
        ↓
kubectl apply
        ↓
AKS Deployment
        ↓
Rolling update

		

The image and Kubernetes configuration should generally be treated as separate concerns.

The container image defines the application artifact.

The Kubernetes manifest defines how that artifact should be deployed.

27. Example Complete Manifest

The following example illustrates a simplified AI inference API deployed to AKS.

			
apiVersion: apps/v1
kind: Deployment
metadata:
  name: inference-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: inference-api
  template:
    metadata:
      labels:
        app: inference-api
    spec:
      containers:
        - name: inference-api
          image: myregistry.azurecr.io/inference-api:1.0
          ports:
            - containerPort: 8080
          resources:
            requests:
              cpu: "250m"
              memory: "256Mi"
            limits:
              cpu: "1"
              memory: "1Gi"
          readinessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: inference-api
spec:
  selector:
    app: inference-api
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

		

Deploy it with:

kubectl apply -f inference-api.yaml

Then verify:

			
kubectl get deployments
kubectl get pods
kubectl get services

The Service can expose the application externally through an Azure load balancer.

28. Important Commands to Know

Command	Purpose
`az aks get-credentials`	Configure `kubectl` to connect to an AKS cluster
`kubectl get nodes`	View cluster nodes
`kubectl apply -f file.yaml`	Create/update resources from a manifest
`kubectl get deployments`	View Deployments
`kubectl get pods`	View Pods
`kubectl get services`	View Services
`kubectl describe pod`	Inspect detailed Pod information
`kubectl describe deployment`	Inspect a Deployment
`kubectl logs`	View container logs
`kubectl get pods -o wide`	View Pods with additional placement/network information
`kubectl delete -f file.yaml`	Delete resources defined by a manifest
`kubectl get events`	View Kubernetes events

A particularly important distinction is:

kubectl apply -f manifest.yaml

is generally preferred for declarative management because it creates or updates the resources described by the manifest.

29. Key Exam Takeaways

For AI-200, make sure you understand these relationships:

Manifest

Defines the desired state of Kubernetes resources.

Deployment

Manages replicated Pods and supports controlled updates.

Pod

The basic execution unit containing one or more containers.

Service

Provides a stable network endpoint for a group of Pods.

Labels

Identify resources.

Selectors

Determine which resources another resource targets.

ConfigMap

Stores non-sensitive configuration.

Secret

Stores sensitive configuration within Kubernetes.

`kubectl apply`

Applies a declarative manifest.

ACR

Commonly stores the container images consumed by AKS.

Readiness probe

Determines whether a Pod should receive traffic.

Liveness probe

Determines whether a container should continue running.

Startup probe

Determines whether an application has successfully started.

`kubectl describe`

Useful for diagnosing Kubernetes resource and scheduling problems.

`kubectl logs`

Useful for diagnosing application/container failures.

Practice Exam Questions

Question 1

You have an AI inference application running in AKS. The application should run three identical instances, and Kubernetes should replace an instance if its Pod fails.

Which Kubernetes resource should you use?

A. Deployment

B. Service

C. ConfigMap

D. Ingress

Answer: A

Explanation

A Deployment manages replicated Pods and maintains the desired number of replicas. If a Pod managed by the Deployment fails, Kubernetes can create a replacement.

A Service provides network access to Pods but doesn’t manage their lifecycle. A ConfigMap stores configuration, and an Ingress manages HTTP/HTTPS routing.

Question 2

An AKS application has a Deployment with the following Pod label:

			
labels:
  app: inference-api

The application is exposed through a Service, but the Service isn’t routing traffic to the Pods.

The Service contains:

			
selector:
  app: ai-service

What should you change?

A. Change the Deployment’s replicas value

B. Change the Service selector to app: inference-api

C. Change the Service type to ClusterIP

D. Change the container’s containerPort

Answer: B

Explanation

The Service selector must match the labels assigned to the target Pods. The Pods are labeled:

app: inference-api

Therefore, the Service should use:

			
selector:
  app: inference-api

Changing the replica count or Service type does not correct the selector mismatch.

Question 3

You have modified a Kubernetes Deployment manifest to use version 2 of an AI inference container:

image: myregistry.azurecr.io/inference-api:v2

What command should you normally use to apply the change?

A. kubectl restart deployment

B. kubectl create deployment

C. kubectl apply -f deployment.yaml

D. az aks update --image v2

Answer: C

Explanation

kubectl apply -f applies the desired state represented by the manifest. When the Deployment’s Pod template changes, Kubernetes can perform a rolling update to replace Pods running the previous image.

Question 4

An AI API deployed to AKS takes several minutes to initialize because it loads a large machine-learning model. Kubernetes is restarting the container before initialization completes.

Which configuration is most appropriate?

A. Increase the Service’s targetPort

B. Add a startup probe

C. Add a ConfigMap

D. Change the Service to LoadBalancer

Answer: B

Explanation

A startup probe is designed for applications that require significant time to initialize. It allows Kubernetes to determine when startup has completed before normal liveness checking takes effect.

A readiness probe controls whether traffic should be sent to the application, while a startup probe is specifically useful during initialization.

Question 5

An AKS Deployment has been created, but its Pods remain in the Pending state.

Which command is most useful for investigating scheduling-related events for a specific Pod?

A. kubectl describe pod <pod-name>

B. kubectl logs <pod-name>

C. kubectl get service <service-name>

D. kubectl apply -f deployment.yaml

Answer: A

Explanation

kubectl describe pod provides detailed information about the Pod, including Kubernetes events. These events can reveal issues such as insufficient resources, unsatisfied scheduling constraints, or volume problems.

kubectl logs is more useful when a container has started and is producing application logs.

Question 6

You want an application in AKS to be reachable from outside the cluster using an Azure-provided external load balancer.

Which Service type should you specify?

A. ClusterIP

B. ExternalName

C. NodePort

D. LoadBalancer

Answer: D

Explanation

A Service with:

type: LoadBalancer

requests an external load balancer through the cloud provider integration. In AKS, this can provide an externally reachable IP address.

ClusterIP is primarily for internal cluster access.

Question 7

An AI application requires the following configuration:

			
MODEL_NAME=customer-support-model
LOG_LEVEL=Information

The values are not sensitive and should be changed independently of the container image.

Which Kubernetes resource is most appropriate?

A. Secret

B. ConfigMap

C. Deployment replica

D. Service

Answer: B

Explanation

A ConfigMap is designed to store non-sensitive configuration data separately from the application container image.

A Kubernetes Secret is intended for sensitive information such as credentials and keys.

Question 8

You have successfully deployed an AKS application, but the Pods show ImagePullBackOff.

The Deployment specifies:

image: myregistry.azurecr.io/inference-api:v5

Which is the most likely category of problem?

A. The Service selector doesn’t match the Pod labels

B. The readiness probe is failing

C. AKS cannot successfully retrieve the specified container image

D. The Deployment has too many replicas

Answer: C

Explanation

ImagePullBackOff indicates that Kubernetes is having difficulty pulling the container image. Potential causes include an incorrect image name or tag, an image that doesn’t exist, or insufficient authorization to access a private registry.

Service selectors and readiness probes are unrelated to the initial image-pull operation.

Question 9

You want an AKS application to receive traffic only after its /health endpoint indicates that it is ready.

Which probe should you configure?

A. Readiness probe

B. Liveness probe

C. Startup probe

D. Resource probe

Answer: A

Explanation

A readiness probe determines whether a container is ready to receive traffic. If the readiness probe fails, Kubernetes can keep the Pod out of the Service’s ready endpoints.

A liveness probe determines whether a container should continue running, while a startup probe is intended to determine whether a slow-starting application has completed initialization.

Question 10

An AKS application is deployed from a YAML file containing both a Deployment and a Service separated by ---.

Which command can be used to create or update both resources according to the manifest?

A. kubectl get -f application.yaml

B. kubectl logs -f application.yaml

C. kubectl describe -f application.yaml

D. kubectl apply -f application.yaml

Answer: D

Explanation

kubectl apply -f processes the resources defined in the manifest and creates or updates them to match the desired state.

The --- separator allows multiple Kubernetes resource definitions to be included in the same YAML file.

Final Review

For this AI-200 topic, the most important thing is to understand how a declarative Kubernetes manifest translates into a running application on AKS.

The core flow is:

			
Container image
      ↓
Azure Container Registry
      ↓
Kubernetes Deployment manifest
      ↓
kubectl apply
      ↓
Deployment
      ↓
ReplicaSet
      ↓
Pods
      ↓
Service
      ↓
Application endpoint

		

When troubleshooting, think systematically:

			
Can't deploy?
    ↓
Can AKS pull the image?
    ↓
Is the Pod scheduled?
    ↓
Is the container starting?
    ↓
Are startup/readiness/liveness probes correct?
    ↓
Do Service selectors match Pod labels?
    ↓
Are ports configured correctly?

		

If you understand that flow—and can recognize Deployment vs. Pod vs. Service vs. ConfigMap vs. Secret, along with the purpose of kubectl apply, labels/selectors, probes, and common Pod states—you will have a strong foundation for the manifest-based AKS questions in AI-200.

Go to the AI-200 Exam Prep Hub main page

azure, Cloud computing, Microsoft Certification August 15, 2026

Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets (AI-200 Exam Prep)

This post is a part of the AI-200: Developing AI Cloud Solutions on Azure  Exam Prep Hub.
This topic falls under these sections:
Develop containerized solutions on Azure (20–25%)
   --> Implement container application hosting
      --> Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Azure App Service is a fully managed platform-as-a-service (PaaS) offering that allows developers to host web applications, APIs, and containerized applications without managing the underlying virtual machines or operating system.

For the AI-200: Developing AI Cloud Solutions on Azure exam, you should understand how to deploy a container image to App Service and, importantly, how to configure the application so that the container receives the configuration, environment variables, and secrets it needs at runtime.

This is particularly important for AI applications because containerized AI workloads commonly need configuration values such as:

Azure AI service endpoints
Model deployment names
Database connection information
Storage account names
Service Bus configuration
Application Insights configuration
Feature flags
API keys or other secrets

A well-designed application should not bake these values into the container image. Instead, configuration should be supplied by the hosting environment, with sensitive values preferably retrieved from a secure secret store such as Azure Key Vault.

1. Understand Azure App Service for Containers

Azure App Service can run applications packaged as custom container images. This allows developers to use their own runtime, dependencies, libraries, and operating-system configuration instead of relying exclusively on App Service’s built-in application stacks.

A typical architecture looks like this:

Developer → Container Image → Container Registry → Azure App Service → Running Container

For example:

A developer creates a Dockerfile.
The Dockerfile is used to build an image.
The image is pushed to Azure Container Registry.
App Service is configured to use that image.
App Service pulls the image.
App Service starts the container.
App Service supplies configuration values as environment variables.
The application reads those values at runtime.

App Service pulls the configured container image when the application starts. If an updated image is pushed to the registry, restarting the application causes App Service to pull the updated image.

This separation between the application image and the application configuration is an important concept for the exam.

2. Why Use Containers with App Service?

A custom container is useful when the application’s requirements don’t fit cleanly into one of App Service’s predefined runtime stacks.

For example, an AI application might require:

A particular Python version
Specific native libraries
Custom machine-learning packages
A specialized web server
OS-level dependencies
A combination of packages that isn’t available in a standard App Service stack

Instead of configuring all those dependencies on the App Service platform, you can package them into a container.

Key benefit

The container provides a consistent application environment.

The same image can potentially be used in:

Development
Testing
Staging
Production
Other container-hosting environments

This supports the important principle:

Build the application once and configure it differently for each environment.

The container should contain the application and its dependencies—not environment-specific secrets.

3. The Container Image and App Service Are Separate Concerns

One of the most important concepts to understand is the difference between the container image and the App Service configuration.

Container image

The image contains things such as:

Application code
Runtime
Dependencies
Libraries
System packages
Startup configuration

App Service configuration

App Service supplies environment-specific information such as:

Database endpoints
API endpoints
Feature flags
Environment names
Secret references
Connection information

This allows the same image to run in multiple environments.

For example:

			
Container Image
    |
    +-- Application code
    +-- Python runtime
    +-- Required libraries
    +-- AI SDKs
    |
    v
App Service
    |
    +-- ENVIRONMENT=Production
    +-- AI_ENDPOINT=...
    +-- MODEL_NAME=...
    +-- DATABASE_CONNECTION=...
    +-- API_KEY=<Key Vault reference>

		

The application doesn’t need a different Docker image simply because it is moving from development to production.

4. Deploying a Container to App Service

There are several ways to deploy a containerized application to App Service.

A common approach is:

			
Dockerfile
    ↓
docker build
    ↓
Container Image
    ↓
Azure Container Registry
    ↓
Azure App Service

		

For example, a container image might be named:

myregistry.azurecr.io/my-ai-api:v1

The registry name identifies the container registry.

The repository identifies the application:

my-ai-api

And the tag identifies a particular version:

v1

Therefore:

myregistry.azurecr.io/my-ai-api:v1

identifies a specific container image.

5. Configure the Container Image

When creating or configuring an App Service application, you specify the container image that App Service should run.

For an image hosted in Azure Container Registry, App Service needs access to the registry.

For a private registry, authentication must be configured.

Depending on the scenario, App Service can use authentication mechanisms such as managed identity rather than embedding registry credentials. Current App Service configuration also supports managed-identity-based access to Azure Container Registry, which is generally preferable to managing long-lived registry passwords.

Exam concept

When you see a question asking for the most secure way to allow App Service to pull a private image from Azure Container Registry, consider:

Managed identity and appropriate Azure role assignments

rather than storing a registry password in application configuration.

6. The Container’s Listening Port

A containerized application must listen on the appropriate port so App Service can route traffic to it.

For custom containers, the port configuration is particularly important.

For example, suppose the application listens on:

The application inside the container needs to listen on that port, and App Service needs to know which port to use.

A common App Service configuration is:

WEBSITES_PORT=8080

The WEBSITES_PORT application setting tells App Service which port the custom container is listening on. Microsoft specifically identifies WEBSITES_PORT as required for custom-container port configuration.

Example

Suppose the Dockerfile contains:

EXPOSE 8080

The application should also actually listen on port 8080.

Then App Service can be configured with:

WEBSITES_PORT = 8080

Important distinction

EXPOSE in a Dockerfile documents the port the container expects to use. It does not by itself guarantee that the application is actually listening on that port.

A common troubleshooting scenario is:

The container starts successfully, but the application isn’t reachable.

One of the first things to verify is whether the application is listening on the expected port and whether WEBSITES_PORT is configured correctly.

7. Environment Variables in App Service

App Service application settings are exposed to applications as environment variables.

This is one of the most important concepts for this exam topic.

For example, you could configure:

			
ENVIRONMENT = Production
MODEL_NAME = gpt-4o-mini
AI_ENDPOINT = https://example.openai.azure.com/

Your application can then read these values from its environment.

For Linux applications and custom containers, App Service passes application settings into the container as environment variables. Changes to App Service settings cause the application to restart.

This allows the application code to remain environment-independent.

8. Why Environment Variables Are Better Than Hard-Coding Configuration

Consider this application code:

AI_ENDPOINT = "https://production-ai.example.com"

This is problematic because the endpoint is embedded in the application.

A better approach is:

			
import os
AI_ENDPOINT = os.environ["AI_ENDPOINT"]

Then App Service supplies:

AI_ENDPOINT=https://production-ai.example.com

The same container can then be deployed elsewhere with:

AI_ENDPOINT=https://development-ai.example.com

without rebuilding the image.

This supports:

Environment portability
Easier deployments
Configuration management
Separation of code and configuration
Safer secret handling

9. Configure Application Settings

App Service application settings can be configured through the Azure portal, Azure CLI, PowerShell, ARM/Bicep, or other deployment mechanisms.

In the Azure portal, application settings are managed under the app’s environment/configuration settings.

For example, you might define:

Setting	Example value	Sensitive?
`APP_ENVIRONMENT`	`Production`	No
`AI_ENDPOINT`	`https://my-ai.openai.azure.com/`	Usually no
`MODEL_NAME`	`chat-model`	No
`LOG_LEVEL`	`Information`	No
`DATABASE_CONNECTION`	Connection information	Potentially
`API_KEY`	Secret value	Yes

App Service stores app settings encrypted at rest. However, for secrets that require centralized secret management, Microsoft recommends using Azure Key Vault references rather than directly storing the secret value in the App Service setting.

10. Secrets Should Not Be Baked into Container Images

This is a major security principle.

Avoid putting something like this in a Dockerfile:

ENV API_KEY="abc123secret"

Also avoid:

API_KEY = "abc123secret"

Why?

Because the secret can potentially become part of the image or source code and therefore propagate into:

Container registries
Image layers
Source repositories
Build systems
Developer machines
Backups
Logs

Instead:

			
Container Image
      +
App Service Configuration
      +
Azure Key Vault

		

should provide the necessary runtime configuration.

11. Azure Key Vault Integration

Azure Key Vault provides centralized management for secrets, keys, and certificates.

For App Service, Key Vault can be integrated using Key Vault references.

Instead of putting the actual secret into an App Service setting, the setting contains a reference to the secret.

Conceptually:

			
API_KEY
    ↓
@Microsoft.KeyVault(...)
    ↓
Azure Key Vault
    ↓
Secret value
    ↓
Application

		

The application can consume the resolved value as an ordinary environment variable.

One of the major benefits is that application code doesn’t need to contain Key Vault-specific retrieval logic just to consume a referenced application setting.

12. Key Vault References

A Key Vault reference has a format similar to:

@Microsoft.KeyVault(SecretUri=https://myvault.vault.azure.net/secrets/mysecret)

Alternatively, a reference can use the vault and secret names:

@Microsoft.KeyVault(VaultName=myvault;SecretName=mysecret)

A specific secret version can also be specified.

For example:

			
AI_API_KEY =
@Microsoft.KeyVault(VaultName=myvault;SecretName=AI-API-Key)

The application can continue to access the setting using the environment variable:

AI_API_KEY

The application doesn’t have to know that the value originated from Key Vault.

13. Managed Identity and Key Vault

For Key Vault references to work securely, App Service needs an identity that can access the Key Vault.

A recommended architecture is:

			
Azure App Service
       |
       | Managed Identity
       |
       v
Azure Key Vault
       |
       v
Secret

		

The application does not need to store a Key Vault username/password or service principal secret.

App Service Key Vault references use the app’s system-assigned managed identity by default, although a user-assigned managed identity can also be configured. The identity must have permission to read secrets from the vault. With Azure RBAC, the Key Vault Secrets User role is an appropriate role for reading secrets.

14. System-Assigned vs. User-Assigned Managed Identity

You should understand the difference for exam questions.

System-assigned managed identity

The identity is tied to the Azure resource.

For example:

			
App Service
    |
    +-- System-assigned identity

If the App Service is deleted, the identity is also deleted.

User-assigned managed identity

The identity is a separate Azure resource.

			
User-assigned identity
       |
       +---- App Service A
       |
       +---- App Service B

		

It can therefore be reused by multiple resources.

Exam consideration

If a scenario specifically requires an identity to exist before the application is created or requires reuse across several resources, a user-assigned managed identity may be more appropriate.

15. Key Vault Secret Rotation

Key Vault references can simplify secret rotation.

When a Key Vault reference doesn’t specify a particular secret version, App Service can use the latest version of the secret.

App Service caches Key Vault reference values and periodically refreshes them. Microsoft documents a refresh interval of up to 24 hours; configuration changes that restart the app can cause the references to be fetched immediately.

This is an important distinction:

Changing the secret in Key Vault does not necessarily mean that the application immediately receives the new value.

If an application must immediately consume a new value, you need to account for the Key Vault reference refresh behavior.

16. What Happens When a Key Vault Reference Fails?

Suppose App Service has:

			
AI_API_KEY =
@Microsoft.KeyVault(VaultName=myvault;SecretName=AI-Key)

but the managed identity doesn’t have permission to retrieve the secret.

The reference might fail to resolve.

Potential causes include:

Incorrect Key Vault name
Incorrect secret name
Secret deleted
Incorrect reference syntax
Managed identity not enabled
Missing Key Vault permissions
Network restrictions preventing access to Key Vault

App Service provides Key Vault reference resolution information that can help diagnose these problems.

Exam clue

If a question says:

The application receives the literal @Microsoft.KeyVault(...) value instead of the expected secret.

Think:

The Key Vault reference failed to resolve.

Then investigate identity, permissions, reference syntax, secret existence, and networking.

17. App Settings vs. Key Vault

A useful exam distinction is:

Requirement	Recommended approach
Non-sensitive configuration	App Service application setting
Environment-specific value	App Service application setting
Secret value	Azure Key Vault
Secret consumed as an environment variable	Key Vault reference in an App Service setting
Shared centralized configuration	Azure App Configuration
Application code	Do not hard-code secrets

App Service application settings are appropriate for ordinary configuration.

Key Vault should be preferred when the value is a secret requiring centralized secret management, access control, auditing, and rotation.

18. App Configuration vs. Key Vault

AI-200 also covers Azure App Configuration, so understand how it differs from Key Vault.

Azure App Configuration

Designed primarily for centralized application configuration.

Examples:

			
Feature flags
Application settings
Environment configuration
Dynamic configuration

Azure Key Vault

Designed for sensitive information such as:

			
Passwords
API keys
Connection secrets
Certificates
Cryptographic keys

		

A common architecture uses both:

                    +---------------------+
                    | Azure App Config     |
                    |                     |
                    | Feature flags       |
                    | Application config  |
                    +----------+----------+
                               |
                               |
Application <------------------+
     |
     |
     +------------------------+
                              |
                              v
                    +---------------------+
                    | Azure Key Vault     |
                    |                     |
                    | API keys            |
                    | Passwords           |
                    | Secrets             |
                    +---------------------+

Do not confuse centralized configuration with secret management.

19. Container Startup Commands

A container has a default startup command defined by its image.

However, App Service can override the startup behavior for a custom container.

This can be useful when:

The container’s default command isn’t appropriate.
The application requires a specific startup command.
Different hosting environments require different startup behavior.

For example:

python app.py

or:

gunicorn --bind 0.0.0.0:8080 app:app

App Service supports specifying a startup command for custom containers.

Exam clue

If a container image works locally but App Service starts it incorrectly, investigate:

Startup command
Listening port
Environment variables
Container logs
Image configuration

20. Environment Variables and Container Startup

Environment variables are available to the application when the container starts.

For example:

			
APP_ENVIRONMENT=Production
PORT=8080
MODEL_NAME=my-model

Your application might use:

			
import os
environment = os.getenv("APP_ENVIRONMENT")
model = os.getenv("MODEL_NAME")

The values can be changed in App Service without changing the container image.

This is especially valuable when promoting the same image through:

			
Development
     ↓
Testing
     ↓
Staging
     ↓
Production

		

Each environment can supply different configuration.

21. App Settings Cause Application Restarts

A frequently tested detail is that changing App Service application settings causes the application to restart.

This matters because configuration changes aren’t necessarily applied to an already-running process without interruption.

Microsoft documents that adding, removing, or modifying app settings causes an App Service app restart.

Therefore, if a scenario says:

An administrator changes an application setting and the application immediately restarts.

That is expected behavior.

22. Container Image Updates

Suppose App Service is configured to run:

myacr.azurecr.io/my-ai-api:latest

A developer builds a new version and pushes it using the same tag.

The registry now contains a newer image associated with latest.

However, simply pushing the new image doesn’t necessarily mean that an already-running container immediately changes.

Restarting the App Service causes it to pull the image again.

This is one reason immutable version tags are often preferable for controlled deployments.

For example:

			
my-ai-api:v1.0.0
my-ai-api:v1.1.0
my-ai-api:v2.0.0

rather than relying exclusively on:

my-ai-api:latest

23. Using `latest` vs. Versioned Tags

`latest`

Advantages:

Simple
Convenient for development

Disadvantages:

Doesn’t clearly identify what is deployed
Makes rollback more difficult
Can make troubleshooting harder
Can introduce unexpected image changes

Versioned tags

For example:

my-ai-api:1.4.2

Advantages:

Clear version identification
Easier rollback
Better deployment traceability
Easier troubleshooting

For production workloads, versioned image tags are generally a better operational practice.

24. Container Logs and Troubleshooting

When a container doesn’t start correctly, examine the container logs.

Common problems include:

Wrong port

The application listens on:

but App Service expects:

Application crashes

For example:

ModuleNotFoundError

or:

Connection refused

Incorrect environment variable

The application expects:

DATABASE_URL

but App Service defines:

DB_URL

Secret resolution failure

The Key Vault reference isn’t resolving.

Startup command failure

The command specified by App Service doesn’t exist or fails.

25. Container Startup Timeout

Custom containers sometimes take longer to initialize than expected.

App Service provides the WEBSITES_CONTAINER_START_TIME_LIMIT setting to control how long the platform waits for a container to start.

The documented default is 230 seconds, with a maximum of 1,800 seconds.

This can matter for AI applications that have relatively large startup workloads.

However, increasing the startup timeout should not be the first response to every startup problem.

First determine why startup is slow.

For example:

Is the container downloading dependencies at startup?
Is the application loading a large model?
Is it waiting for an external service?
Is the application listening on the wrong port?
Is the startup command incorrect?

26. HTTPS and Custom Containers

A custom container doesn’t necessarily need to implement HTTPS itself when hosted through App Service.

App Service can handle HTTPS termination at the platform’s front ends.

Therefore, an application can commonly listen for HTTP inside the container while clients connect to the application through HTTPS.

Conceptually:

			
Client
  |
 HTTPS
  |
  v
App Service
  |
 HTTP
  |
  v
Container

		

This is different from saying that application traffic is universally unprotected in every internal configuration; networking and security architecture still matter.

27. Continuous Deployment for Containers

App Service can integrate with container registries to support automated deployments.

A common flow is:

			
Developer
   |
   v
Source Repository
   |
   v
Build
   |
   v
Container Image
   |
   v
Azure Container Registry
   |
   v
App Service

		

A registry push can be used to trigger a deployment/restart workflow.

App Service supports continuous deployment scenarios involving container registries, including Azure Container Registry.

For production systems, CI/CD is generally preferable to manually rebuilding and deploying containers.

28. A Recommended AI Application Architecture

A reasonable architecture for an AI application hosted in a container on App Service might look like this:

                         Azure Container Registry
                                  |
                                  | Container Image
                                  v
                         +-------------------+
                         |   Azure App       |
                         |     Service       |
                         +---------+---------+
                                   |
                    +--------------+--------------+
                    |                             |
             Environment Variables          Managed Identity
                    |                             |
                    |                             v
                    |                      Azure Key Vault
                    |                             |
                    |                           Secrets
                    |
                    +--------------------+
                                         |
                                         v
                                  AI Application
                                         |
                  +----------------------+----------------+
                  |                      |                 |
                  v                      v                 v
             Azure AI             Azure Database      Azure Storage

The container image contains the application.

App Service provides environment-specific configuration.

Managed identity provides secure access to Azure resources.

Key Vault stores secrets.

This is a strong pattern to recognize in AI-200 scenario questions.

29. Security Best Practices

For the exam, remember these principles.

Don’t hard-code secrets

Avoid:

API_KEY=abc123

inside source code or Dockerfiles.

Don’t put secrets in image layers

Building a secret into an image doesn’t make it secure simply because the image is stored in a private registry.

Use managed identities

When Azure services support Microsoft Entra authentication and managed identities, prefer them over long-lived credentials.

Use Key Vault for secrets

Store sensitive values centrally.

Use least privilege

Grant the App Service identity only the permissions it requires.

Separate environments

Development, testing, and production should have appropriately separated configuration and secrets.

Use versioned images

Prefer:

myapp:1.2.3

over relying exclusively on:

myapp:latest

30. Important AI-200 Exam Concepts to Remember

The following relationships are particularly important:

Concept	Remember
Custom container	Runs your own container image in App Service
Azure Container Registry	Common private registry for App Service container images
App settings	Become environment variables
`WEBSITES_PORT`	Identifies the port used by a custom container
Startup command	Controls/overrides how the container application starts
Key Vault	Secure centralized secret management
Key Vault reference	Allows an App Service setting to reference a Key Vault secret
Managed identity	Avoids storing credentials for Azure resource access
System-assigned identity	Lifecycle tied to the Azure resource
User-assigned identity	Separate reusable identity resource
App setting changes	Cause an application restart
Image update	Restart causes App Service to pull the updated image
`latest`	Convenient but less predictable
Versioned tags	Better traceability and rollback
Container logs	Important for startup/runtime troubleshooting
`WEBSITES_CONTAINER_START_TIME_LIMIT`	Controls custom-container startup wait time

Practice Exam Questions

Question 1

You have a Python-based AI API packaged as a Linux container. The application listens on port 8080 inside the container.

You deploy the container to Azure App Service, but requests to the application fail because App Service cannot connect to the application.

Which App Service setting should you verify first?

A. WEBSITE_RESOURCE_GROUP

B. WEBSITE_SITE_NAME

C. WEBSITES_PORT

D. WEBSITE_SKU

Answer: C

Explanation

For custom containers, App Service needs to know which port the container is listening on. If the application listens on port 8080, configuring:

WEBSITES_PORT=8080

helps App Service route traffic to the correct container port.

The other settings describe the App Service environment but do not determine the container’s application port.

Question 2

An AI application is deployed as a container to Azure App Service. The application requires an API key that changes periodically.

The development team wants to avoid storing the API key in source code, the Dockerfile, or the container image.

Which solution provides the best approach?

A. Store the API key in the Dockerfile as an ENV value.

B. Store the API key in Azure Key Vault and reference it from an App Service application setting.

C. Store the API key in the container image and use a private Azure Container Registry.

D. Store the API key in the application’s source code and protect the repository with RBAC.

Answer: B

Explanation

Azure Key Vault is designed for centralized secret management. App Service can use a Key Vault reference as an application setting, allowing the application to consume the secret as an environment variable without embedding the secret in the image or source code.

A private container registry protects access to the image but does not make secrets embedded inside the image a good security practice.

Question 3

An Azure App Service application uses a Key Vault reference to retrieve an API key. The application is receiving the literal Key Vault reference string rather than the expected secret value.

Which issue should you investigate?

A. Whether the Dockerfile contains an EXPOSE instruction

B. Whether WEBSITES_PORT matches the application port

C. Whether the App Service managed identity has permission to read the Key Vault secret

D. Whether the image uses the latest tag

Answer: C

Explanation

A Key Vault reference must be resolved by App Service. The application’s managed identity needs permission to retrieve the referenced secret.

A missing or incorrectly configured identity, missing Key Vault permissions, an invalid secret name, or other Key Vault configuration problems can prevent resolution.

The port and image tag are unrelated to Key Vault reference resolution.

Question 4

A development team wants to deploy the same container image to development, test, and production environments. The AI endpoint differs between environments.

What is the best approach?

A. Build a separate Docker image for each environment.

B. Store all three endpoints in the Dockerfile and select one at runtime.

C. Create separate source-code branches containing different endpoint values.

D. Store the endpoint as an App Service application setting in each environment.

Answer: D

Explanation

Environment-specific configuration should be separated from the application image.

Each App Service environment can provide its own application setting:

AI_ENDPOINT=https://development...

or:

AI_ENDPOINT=https://production...

The same container image can therefore be deployed across environments.

Question 5

A developer pushes a new version of an image to Azure Container Registry using the same latest tag that an App Service application is already configured to use.

When should the developer expect App Service to retrieve the updated image?

A. When the running container is restarted

B. Immediately when the image is pushed

C. Only when the App Service plan is resized

D. Only after the image tag is deleted

Answer: A

Explanation

App Service pulls the configured container image when the application starts. If an updated image is pushed using the same tag, restarting the App Service causes the updated image to be pulled.

This is one reason explicit version tags are often preferable for controlled production deployments.

Question 6

An organization wants an App Service application to retrieve secrets from Azure Key Vault without storing a Key Vault password or service principal secret in the application.

Which feature should be used?

A. Docker ENV instructions

B. Managed identity

C. App Service startup command

D. Container port mapping

Answer: B

Explanation

Managed identity allows Azure resources such as App Service to authenticate to supported Azure services without requiring developers to store credentials in application configuration.

For Key Vault references, App Service can use its system-assigned managed identity by default or a configured user-assigned identity.

Question 7

An AI container deployed to App Service takes approximately five minutes to initialize because it performs a large initialization operation before listening for HTTP traffic.

The platform terminates the container before initialization completes.

Which setting can be used to increase the amount of time App Service waits for the container to start?

A. WEBSITES_PORT

B. WEBSITE_SITE_NAME

C. WEBSITES_CONTAINER_START_TIME_LIMIT

D. WEBSITE_WARMUP_PATH

Answer: C

Explanation

WEBSITES_CONTAINER_START_TIME_LIMIT controls how long App Service waits for a custom container to start.

The documented default is 230 seconds and the maximum is 1,800 seconds.

However, increasing the timeout should be done only after determining that the startup delay is legitimate rather than caused by a configuration or application problem.

Question 8

An application administrator changes the value of an App Service application setting.

What should the administrator expect?

A. The setting changes only the next time a new container image is deployed.

B. The setting changes the Dockerfile stored in Azure Container Registry.

C. App Service restarts the application so that the new setting can be supplied to the application environment.

D. The setting automatically modifies the source code in the application repository.

Answer: C

Explanation

App Service application settings are supplied to the application as environment variables. Changes to application settings cause the application to restart, allowing the new configuration to be supplied to the running application.

The setting does not modify the container image, Dockerfile, or source repository.

Question 9

You are designing a production AI application running in a custom container on Azure App Service. The application requires an API key.

Which design provides the strongest separation between application code and the secret?

A. Store the secret in Azure Key Vault and expose it to the application through an App Service Key Vault reference.

B. Store the secret in the Dockerfile using an ENV instruction.

C. Store the secret in a text file inside the container image.

D. Store the secret in the application’s source code and restrict repository access.

Answer: A

Explanation

A Key Vault reference allows the secret to remain in Azure Key Vault while the application consumes it through an App Service configuration setting.

This provides better separation between:

Application code
Container image
Deployment configuration
Secrets

The App Service managed identity can be granted the minimum required permissions to retrieve the secret.

Question 10

An organization has multiple App Service applications that need to use the same identity when accessing Azure Key Vault. The identity must also be able to exist independently of the lifecycle of any individual App Service application.

Which type of managed identity should be used?

A. System-assigned managed identity

B. App Service publishing credentials

C. User-assigned managed identity

D. Container registry administrator credentials

Answer: C

Explanation

A user-assigned managed identity is a standalone Azure resource that can be assigned to multiple Azure resources.

This makes it appropriate when:

Multiple applications need the same identity.
The identity needs an independent lifecycle.
The identity must exist before an application is created.
The organization wants to reuse the identity across resources.

A system-assigned identity is tied to the lifecycle of its associated Azure resource.

Final Exam Takeaways

For AI-200, the most important mental model is:

The container image contains the application; App Service supplies the environment-specific configuration; Key Vault protects sensitive values; managed identity provides secure access to Azure resources.

When you encounter an exam scenario, think through the problem in this order:

Where is the container image?
- Azure Container Registry?
- Another private registry?
- Public registry?
Can App Service pull the image?
- Is authentication configured?
- Would managed identity be appropriate?
What port does the application actually listen on?
- Does it match the App Service configuration?
- Is WEBSITES_PORT configured appropriately?
How does the application receive configuration?
- App Service application settings
- Environment variables
Does the configuration contain a secret?
- Use Azure Key Vault rather than embedding the secret in the image or source code.
How does App Service access Key Vault?
- Managed identity
- Appropriate Key Vault permissions
Is the container starting correctly?
- Startup command
- Container logs
- Port
- Environment variables
- Startup timeout
How is the image version managed?
- Prefer identifiable/versioned image tags for production deployments.
- Understand what happens when an image behind a tag is replaced.

These distinctions—particularly App Service settings vs. container image contents, environment variables vs. secrets, Key Vault references vs. hard-coded credentials, and system-assigned vs. user-assigned managed identities—are exactly the kinds of distinctions that can turn a plausible answer into the correct AI-200 answer.

Go to the AI-200 Exam Prep Hub main page

AI-200, azure, Cloud computing, Microsoft Certification August 15, 2026

Build and Run Images by Using Azure Container Registry Tasks (AI-200 Exam Prep)

This post is a part of the AI-200: Developing AI Cloud Solutions on Azure  Exam Prep Hub.
This topic falls under these sections:
Develop containerized solutions on Azure (20–25%)
   --> Implement container application hosting
      --> Build and Run Images by Using Azure Container Registry Tasks

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Azure Container Registry Tasks (ACR Tasks) provides cloud-based capabilities for building, testing, and managing container images in Azure Container Registry (ACR).

ACR Tasks is particularly useful when developers want to move container image builds into the cloud rather than relying on a locally installed Docker engine. It can support simple on-demand builds, automated builds triggered by source-code or base-image changes, and more sophisticated multi-step workflows involving multiple containers.

For the AI-200: Developing AI Cloud Solutions on Azure exam, you should understand not only how to execute an ACR Task, but also when to use each type of task, how build contexts work, how images are tagged, how multi-step tasks are defined, how tasks are triggered, and how tasks can securely access other resources.

Microsoft’s AI-200 training specifically identifies building and managing container images in the cloud with ACR Tasks and using the Azure CLI to run ACR quick tasks as learning objectives.

1. What Are Azure Container Registry Tasks?

ACR Tasks is a collection of capabilities within Azure Container Registry that allows you to perform container image operations in Azure.

At a high level:

			
Source Code / Dockerfile
          |
          v
     ACR Task
          |
    +-----+-----+
    |           |
    v           v
   Build       Test
    |           |
    +-----+-----+
          |
          v
    Container Image
          |
          v
          ACR

		

ACR Tasks can:

Build container images in Azure
Push images to ACR
Run containers as part of a task
Test container images
Build multiple images
Execute steps sequentially or in parallel
Automatically trigger builds from source-code changes
Automatically rebuild images when base images change
Run tasks on a schedule
Integrate into CI/CD workflows

ACR Tasks supports Linux, Windows, and ARM image platforms, depending on the configuration and supported scenarios.

2. Why Use ACR Tasks?

A traditional container development workflow might look like this:

			
Developer Computer
       |
       +-- Docker build
       |
       +-- Docker test
       |
       +-- Docker push
       |
       v
Azure Container Registry

		

This requires the developer’s machine to have the appropriate container tooling.

With an ACR Task:

			
Developer
    |
    | Azure CLI
    v
Azure Container Registry
    |
    +-- Build
    +-- Test
    +-- Push

		

The build is performed in Azure.

This has several advantages:

No local Docker Engine is required for an ACR quick task.
Builds can be standardized.
Builds can be automated.
Container images can be built close to the registry.
Build workflows can be triggered by source-code changes.
Base-image updates can automatically initiate rebuilds.
More complex build/test workflows can be defined using YAML.

Microsoft describes quick tasks as an integrated development experience that offloads container image builds to Azure and can perform the equivalent of docker build and docker push in the cloud.

3. Three Important ACR Task Scenarios

For AI-200, understand these three categories:

Task type	Primary purpose
Quick task	On-demand build and push
Automatically triggered task	Automatically execute when an event occurs
Multi-step task	Build, test, run, and push multiple images/workflows

These aren’t mutually exclusive concepts.

For example, a multi-step task can also be automatically triggered by a Git commit.

4. Quick Tasks

A quick task is an on-demand container image build performed in Azure.

It is particularly useful during development.

The Azure CLI command is:

az acr build

For example:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

The final . represents the build context.

Conceptually, this performs:

			
Dockerfile + build context
          |
          v
      ACR Task
          |
          v
     Build image
          |
          v
      Push image
          |
          v
          ACR

		

The important point is that the build takes place in Azure rather than requiring a local Docker engine.

ACR Tasks’ quick-build capability is essentially a cloud-based equivalent of performing a Docker build and push operation.

5. Understanding the Build Context

One of the most important concepts when using az acr build is the build context.

Consider:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

The . specifies the current directory as the build context.

The build context contains files that are available to the Docker build process.

For example:

			
orders-api/
│
├── Dockerfile
├── requirements.txt
├── app.py
└── src/

		

Running:

az acr build ... .

makes that directory the build context.

The context can also come from other supported locations, including source repositories.

6. Dockerfile and ACR Tasks

ACR Tasks uses familiar Docker build syntax.

For example:

			
FROM python:3.12
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]

		

You can then build the image with:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

The Dockerfile defines how the container image is constructed.

ACR Tasks handles the build environment and performs the build in Azure.

7. Specifying a Dockerfile

If the Dockerfile has a different name or location, specify it using --file.

For example:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    --file Dockerfile.production \
    .

		

You can also specify a Dockerfile located elsewhere relative to the build context.

The important exam concept is:

The build context and Dockerfile are related but are not necessarily the same thing.

The Dockerfile describes the build instructions.

The build context identifies the files available to the build.

8. ACR Tasks Versus Local Docker Builds

Consider this traditional command:

docker build -t orders-api:v1 .

With ACR Tasks, you can use:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

The conceptual difference is:

Local Docker	ACR Tasks
Build occurs locally	Build occurs in Azure
Requires Docker Engine	No local Docker Engine required for quick tasks
Image initially exists locally	Image can be pushed directly to ACR
Developer manages build environment	Azure provides the task execution environment

This distinction is a likely source of scenario-based exam questions.

9. Building Without a Local Docker Engine

Suppose a developer has:

Azure CLI
Access to an Azure Container Registry
A Dockerfile
Application source code

but doesn’t have Docker installed.

The developer can still build the image using:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

This is one of the strongest scenarios for recognizing ACR Tasks on the exam.

10. Running an Image with ACR Tasks

ACR Tasks can also run containers as part of a task.

The cmd step is used for this purpose in multi-step tasks.

For example:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .
  - cmd: $Registry/orders-api:$ID

The cmd step runs a container using the specified image.

This makes it possible to use ACR Tasks for testing.

For example:

			
Build image
    |
    v
Run image
    |
    v
Execute tests
    |
    v
Push image

		

The cmd step supports parameters similar to familiar container-run operations, including environment variables and detached execution.

11. Multi-Step Tasks

A multi-step task allows you to create a more sophisticated container workflow.

Instead of simply:

Build → Push

you can implement:

			
Build
  |
  v
Run
  |
  v
Test
  |
  v
Push

		

You can also build multiple images:

             +--> Build API ----+
             |                  |
Source ------+                  +--> Test --> Push
             |                  |
             +--> Build Worker -+

Multi-step tasks are defined in a YAML file.

Microsoft identifies three primary ACR Tasks step types:

build
push
cmd

12. The `build` Step

The build step builds a container image.

Example:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .

Conceptually, this is similar to:

docker build

but the build is performed within the ACR Tasks environment.

The image name should identify the image that the task builds.

13. The `push` Step

The push step pushes an image to a container registry.

Example:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .
  - push:
      - $Registry/orders-api:$ID

		

The build step creates the image.

The push step publishes it to the registry.

An important exam distinction is that in a multi-step az acr run task, you should not assume that a built image is automatically pushed simply because it was built. The task definition can explicitly use a push step to publish it.

14. The `cmd` Step

The cmd step executes a container.

For example:

			
version: v1.1.0
steps:
  - cmd: bash:3.0 echo "Hello from ACR Tasks"

It can also execute an image produced by an earlier build:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .
  - cmd: $Registry/orders-api:$ID

This is especially useful for testing.

The cmd step can use environment variables and other execution options.

15. Build, Test, and Push

A common ACR Tasks pattern is:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .
  - cmd: $Registry/orders-api:$ID
  - push:
      - $Registry/orders-api:$ID

		

Conceptually:

             BUILD
               |
               v
          Container Image
               |
               v
              TEST
               |
         Tests successful
               |
               v
              PUSH
               |
               v
              ACR

This pattern can prevent an image from being pushed until validation has occurred.

16. Step Dependencies

ACR Tasks allows steps to have dependencies.

The when property can specify which previous steps must complete before a step executes.

For example:

			
version: v1.1.0
steps:
  - id: build
    build: -t $Registry/orders-api:$ID .
  - id: test
    cmd: $Registry/orders-api:$ID
    when: ["build"]
  - id: push
    push:
      - $Registry/orders-api:$ID
    when: ["test"]

		

The sequence is:

			
build
  |
  v
test
  |
  v
push

		

This allows the task to express workflow dependencies explicitly.

17. Parallel Execution

ACR Tasks can also execute independent steps concurrently.

For example:

			
version: v1.1.0
steps:
  - id: build-api
    build: -t $Registry/api:$ID .
    when: ["-"]
  - id: build-worker
    build: -t $Registry/worker:$ID ./worker
    when: ["-"]

		

The special:

when: ["-"]

indicates that the step has no dependency on another step and can begin immediately.

Therefore:

        +--> Build API ---+
        |                 |
START --+                 +--> Continue
        |                 |
        +--> Build Worker-+

This can reduce total task execution time when operations are independent.

Microsoft’s ACR Tasks YAML reference specifically documents when: ["-"] for steps that have no dependency and can execute concurrently.

18. Build Dependencies Versus Sequential Steps

If when isn’t specified, a step is dependent on the previous step in the task definition.

For example:

			
steps:
  - id: build
    build: -t $Registry/api:$ID .
  - id: test
    cmd: $Registry/api:$ID
  - id: push
    push:
      - $Registry/api:$ID

		

This naturally produces:

build → test → push

If explicit dependencies are needed, use when.

19. Running an ACR Task

The Azure CLI command commonly used to execute a task definition is:

az acr run

For example:

			
az acr run \
    --registry myregistry \
    --file acr-task.yaml \
    .

You can also use a Git repository as the context.

For example:

			
az acr run \
    --registry myregistry \
    --file acr-task.yaml \
    https://github.com/example/project.git

The task receives the specified source context and executes the defined workflow.

20. `az acr build` Versus `az acr run`

This is an important distinction for AI-200.

`az acr build`

Designed primarily for a quick cloud-based image build.

Example:

			
az acr build \
    --registry myregistry \
    --image orders-api:v1 \
    .

Think:

Build an image quickly in Azure.

`az acr run`

Executes an ACR Tasks workflow.

Example:

			
az acr run \
    --registry myregistry \
    --file acr-task.yaml \
    .

Think:

Run a defined task workflow.

A multi-step task uses az acr run.

21. ACR Tasks Run Variables

ACR Tasks provides built-in run variables.

These variables can be used to create standardized image names and tags.

One particularly useful variable is:

Run.ID

which can be represented in task YAML using the $ID alias.

For example:

			
steps:
  - build: -t $Registry/orders-api:$ID .

This gives each task run a unique identifier that can be incorporated into the image tag.

ACR Tasks also provides variables associated with:

Registry
Registry name
Run ID
Date
Operating system
Architecture
Git commit
Git branch
Task name

22. Why Use Unique Build Tags?

Suppose every build uses:

orders-api:latest

You lose an easy way to distinguish individual builds.

Instead, you could use:

			
orders-api:build-123
orders-api:build-124
orders-api:build-125

ACR Tasks’ run ID can help automate this.

For example:

			
steps:
  - build: -t $Registry/orders-api:$ID .
  - push:
      - $Registry/orders-api:$ID

This produces unique image references for individual runs.

This is especially useful for CI/CD scenarios.

23. Automatically Triggered Tasks

ACR Tasks can automatically execute based on events.

Important trigger scenarios include:

Source-code updates

A task can run when code is committed to a supported Git repository.

For example:

			
Developer commits code
        |
        v
Git repository
        |
        v
ACR Task trigger
        |
        v
Build image
        |
        v
Push image

		

Base-image updates

A task can be triggered when a base image changes.

For example:

FROM python:3.12

If the base image is updated, an ACR Task can rebuild the application image.

This is useful for automatically incorporating updated OS or framework components.

Scheduled execution

ACR Tasks can also support scheduled execution.

For example:

			
Every night
     |
     v
ACR Task
     |
     v
Build/test image

		

Microsoft documents source-code, base-image, and timer-based triggers as ACR Tasks automation scenarios.

24. Base Image Update Triggers

Base image triggers are especially relevant to security and maintenance.

Suppose:

FROM ubuntu:24.04

A security update causes a newer version of the base image to become available.

Without automation:

			
Base image updated
       |
       X
Application image remains unchanged

With an ACR Task:

			
Base image updated
       |
       v
ACR Task trigger
       |
       v
Rebuild application image
       |
       v
Push updated image

		

This allows organizations to automatically rebuild images when their dependencies change.

Microsoft describes this scenario as a way to automate OS and framework patching for container images.

25. Source-Code Triggers

ACR Tasks can integrate with source repositories.

For example:

			
Git commit
    |
    v
ACR Task
    |
    +-- Build
    +-- Test
    +-- Push

		

This provides a simple cloud-based container CI workflow.

A task can be configured to respond to commits and, depending on the configuration, pull-request activity in supported Git repositories.

26. Multi-Container Workflows

ACR Tasks becomes particularly valuable when an application contains multiple containers.

Suppose you have:

			
Web API
Worker
Test suite

You could define:

			
Build API
    |
Build Worker
    |
Run tests
    |
Push API
    |
Push Worker

		

Or independent builds could execute concurrently:

            +--> Build API -----+
            |                   |
START ------+                   +--> Test --> Push
            |                   |
            +--> Build Worker --+

Multi-step tasks are designed specifically for these types of workflows.

27. ACR Tasks and CI/CD

ACR Tasks can be incorporated into a broader CI/CD architecture.

For example:

			
Developer
    |
    v
Git Repository
    |
    v
ACR Task
    |
    +--> Build
    |
    +--> Test
    |
    +--> Push
    |
    v
Azure Container Registry
    |
    v
Container Apps / AKS / App Service

		

ACR Tasks is therefore not merely a command for building images. It can serve as a container lifecycle building block within an automated development process.

28. Accessing Other Registries

An ACR Task may need to access images or artifacts outside the registry where the task runs.

For example:

			
ACR Task
   |
   | Pull base image
   v
External Registry

		

or:

			
ACR Task
   |
   | Push image
   v
Another Registry

		

ACR Tasks supports authentication mechanisms for accessing protected resources.

Managed identities are particularly useful when an ACR Task needs to access other Azure resources without embedding credentials in the task definition.

Microsoft documents both system-assigned and user-assigned managed identities for ACR Tasks.

29. Managed Identities for ACR Tasks

An ACR Task can have a managed identity.

Two types are available:

System-assigned managed identity

The identity is associated with the specific ACR Task resource.

Its lifecycle is tied to that resource.

User-assigned managed identity

The identity is an independent Azure resource that can be assigned to multiple resources.

This can be useful when the same identity needs to be reused.

The key exam concept is:

Managed identities allow ACR Tasks to access protected Azure resources without embedding credentials in the task definition.

30. ACR Tasks and Azure Key Vault

ACR Tasks can also integrate with Azure Key Vault for scenarios where a task needs access to secrets.

A secure architecture might look like:

                 Azure Key Vault
                       |
                       | Secret
                       v
ACR Task ------ Managed Identity
                       |
                       v
                  Build/Test

This is preferable to hard-coding credentials into Dockerfiles, scripts, or task definitions.

31. Security Considerations

When designing ACR Task workflows:

Avoid putting secrets directly on command lines

Command-line arguments can potentially be captured by diagnostic or logging systems.

Avoid embedding credentials in Dockerfiles

A Dockerfile should not contain permanent passwords, tokens, or keys.

Prefer managed identities

When the target resource supports identity-based authentication, managed identities reduce credential-management overhead.

Use least privilege

Give the task only the permissions it needs.

Be careful with external registry credentials

If a task must access another private registry, configure authentication appropriately rather than placing credentials in source code.

Microsoft specifically warns that information supplied through command lines or URIs can appear in ACR diagnostic tracing, including sensitive values.

32. Task YAML Structure

A basic multi-step task looks like:

			
version: v1.1.0
steps:
  - build: -t $Registry/orders-api:$ID .
  - push:
      - $Registry/orders-api:$ID

		

A more sophisticated task could look like:

			
version: v1.1.0
steps:
  - id: build-api
    build: -t $Registry/orders-api:$ID .
  - id: test-api
    cmd: $Registry/orders-api:$ID
    when: ["build-api"]
  - id: push-api
    push:
      - $Registry/orders-api:$ID
    when: ["test-api"]

		

The key elements are:

Element	Purpose
`version`	YAML task format version
`steps`	Defines task operations
`build`	Builds an image
`push`	Pushes an image
`cmd`	Runs a container
`id`	Gives a step an identifier
`when`	Defines dependencies
`$Registry`	Registry run-variable alias
`$ID`	Run ID alias

ACR Tasks currently supports YAML as the task-definition format.

33. A Complete Build-Test-Push Example

Consider an API application with:

			
Dockerfile
src/
tests/

A multi-step task could conceptually perform:

			
version: v1.1.0
steps:
  - id: build
    build: -t $Registry/orders-api:$ID .
  - id: test
    cmd: $Registry/orders-api:$ID
    when: ["build"]
  - id: push
    push:
      - $Registry/orders-api:$ID
    when: ["test"]

		

The workflow becomes:

                  +----------------+
                  |     Source     |
                  +-------+--------+
                          |
                          v
                       BUILD
                          |
                          v
                    Container Image
                          |
                          v
                        TEST
                          |
                    Tests pass
                          |
                          v
                        PUSH
                          |
                          v
                         ACR

This is an excellent pattern to recognize in scenario-based questions.

34. `az acr build` Versus Multi-Step Tasks

A useful exam comparison is:

Requirement	Appropriate approach
Build one image now	`az acr build`
Build image without local Docker	`az acr build`
Build and push a simple image	Quick task
Build and test an image	Multi-step task
Build several images	Multi-step task
Run a container during a workflow	`cmd` step
Push an image from a multi-step task	`push` step
Trigger from Git commit	Automatically triggered ACR Task
Rebuild when base image changes	Base-image trigger
Run periodically	Scheduled task

35. Common Exam Traps

Trap 1: Choosing Azure Container Instances

ACR Tasks is about building and managing container image workflows.

Azure Container Instances is primarily about running containers.

If the question says:

“Build a container image in Azure without installing Docker locally.”

Think:

ACR Tasks

not Azure Container Instances.

Trap 2: Confusing ACR with ACR Tasks

ACR is the registry.

ACR Tasks provides cloud-based build and automation capabilities.

Think:

			
ACR
 ↓
Store images
ACR Tasks
 ↓
Build/test/automate images

		

Trap 3: Assuming `az acr run` and `az acr build` are identical

They are not.

az acr build is designed for the quick cloud build scenario.

az acr run executes a task definition or command in the ACR Tasks environment.

Trap 4: Assuming every build automatically pushes an image

For a quick az acr build, the resulting image is pushed to the registry by default.

For an az acr run multi-step task, you should explicitly define a push step when you want to push the built image.

This distinction is explicitly documented in the ACR Tasks YAML reference.

Trap 5: Using `cmd` when you need to build an image

cmd runs a container.

build builds a container image.

Remember:

			
build → create image
cmd   → run container
push  → publish image

Trap 6: Ignoring the build context

The build context determines what files are available to the Docker build.

A Dockerfile alone isn’t necessarily sufficient if it references files from the context.

Trap 7: Putting secrets in the Dockerfile

Never assume that a secret belongs in:

ENV PASSWORD=...

or:

RUN some-command --password ...

Use appropriate Azure identity and secret-management mechanisms instead.

36. AI-200 Exam-Focused Review

Make sure you understand the following:

ACR Tasks

Cloud-based container build and automation capabilities.

Quick task

On-demand image build, commonly using:

az acr build

`az acr run`

Executes an ACR task workflow or command.

Build context

The files supplied to the container build.

`build`

Builds a container image.

`push`

Pushes an image to a registry.

`cmd`

Runs a container as part of a task.

`when`

Defines dependencies between task steps.

`$Registry`

Identifies the registry associated with the task run.

`$ID`

Identifies the current task run and can be used to generate unique tags.

Multi-step task

Supports complex workflows involving building, testing, running, and pushing containers.

Source trigger

Automatically runs a task when supported source-code changes occur.

Base-image trigger

Automatically rebuilds images when a base image changes.

Scheduled trigger

Runs tasks according to a schedule.

Managed identity

Allows a task to access protected Azure resources without embedding credentials.

37. The Mental Model to Remember

For AI-200, think of ACR Tasks as a cloud-based container build and automation engine attached to Azure Container Registry.

                    SOURCE
                       |
             +---------+---------+
             |                   |
          Dockerfile          Git Repo
             |                   |
             +---------+---------+
                       |
                       v
                  ACR TASK
                       |
          +------------+------------+
          |            |            |
        BUILD         CMD         PUSH
          |            |            |
          |          TEST           |
          |            |            |
          +------------+------------+
                       |
                       v
                  ACR IMAGE
                       |
                       v
             Container Service
        +----------+----------+
        |          |          |
       AKS    Container Apps  App Service

The most important distinction is:

ACR stores the image; ACR Tasks builds, tests, and automates the image lifecycle.

Practice Exam Questions

Question 1

A developer has a Dockerfile and application source code but does not have Docker installed locally. The developer needs to build the image in Azure and store it in an Azure Container Registry.

Which command should the developer use?

A. az container create

B. az acr repository create

C. az acr build

D. az aks create

Answer: C

Explanation: az acr build performs a cloud-based container image build using Azure Container Registry Tasks. The build occurs in Azure, so a local Docker Engine isn’t required for this scenario. The command can build and push the resulting image to ACR.

Question 2

A development team wants to create an automated workflow with the following steps:

Build an API container image.
Run the image.
Execute functional tests.
Push the image only if the tests succeed.

Which ACR Tasks capability should be used?

A. A multi-step task

B. ACR geo-replication

C. An ACR repository

D. An Azure Container Apps revision

Answer: A

Explanation: Multi-step ACR Tasks are designed for workflows that combine multiple container operations. The build, cmd, and push step types can be combined, and dependencies can be defined using the when property. This allows testing to occur before the image is pushed.

Question 3

An ACR Task contains the following YAML:

			
steps:
  - id: build
    build: -t $Registry/api:$ID .
  - id: test
    cmd: $Registry/api:$ID
    when: ["build"]
  - id: push
    push:
      - $Registry/api:$ID
    when: ["test"]

		

What is the purpose of when: ["test"] on the final step?

A. It causes the push to run before testing

B. It causes the push to run concurrently with testing

C. It causes the push step to be skipped

D. It makes the push step dependent on successful completion of the test step

Answer: D

Explanation: The when property establishes dependencies between task steps. Here, the push step depends on the step identified as test, so it won’t execute until the test step completes successfully.

Question 4

An organization wants an ACR Task to automatically rebuild application images whenever a new version of a base image becomes available.

Which trigger should be configured?

A. A repository namespace trigger

B. A base-image update trigger

C. A container restart trigger

D. An Azure Monitor alert trigger

Answer: B

Explanation: ACR Tasks supports base-image update triggers. When the configured base image changes, the task can automatically rebuild the application image. This is particularly useful for incorporating updated operating-system and framework components.

Question 5

An ACR Task needs to execute two independent image builds at the same time. Which YAML configuration allows the steps to start without depending on another task step?

A. when: ["-"]

B. when: ["parallel"]

C. when: ["async"]

D. when: ["none"]

Answer: A

Explanation: In ACR Tasks, when: ["-"] indicates that the step has no dependency on another step and can begin immediately. This can allow independent steps to execute concurrently.

Question 6

A developer wants to create a unique container image tag for every ACR Task execution. Which ACR Tasks variable is specifically designed to identify the current task run?

A. $Branch

B. $Registry

C. $ID

D. $Architecture

Answer: C

Explanation: $ID is an ACR Tasks alias for the current run ID. It can be used to create unique image tags, such as:

-t $Registry/api:$ID

This is useful for distinguishing images produced by different task executions.

Question 7

A multi-step ACR Task has the following steps:

			
steps:
  - build: -t $Registry/api:$ID .
  - push:
      - $Registry/api:$ID

What is the primary purpose of the push step?

A. Run the container

B. Upload the built image to a container registry

C. Compile the Dockerfile

D. Create an Azure Container Apps revision

Answer: B

Explanation: The push step publishes a built or retagged container image to a container registry. The build step creates the image; push publishes it.

Question 8

An organization wants an ACR Task to execute whenever developers commit code to a supported Git repository. Which capability should be configured?

A. A source-code trigger

B. An ACR retention policy

C. An ACR private endpoint

D. A container health probe

Answer: A

Explanation: ACR Tasks supports source-code triggers that can automatically execute builds or multi-step tasks when changes occur in supported Git repositories. This provides a simple mechanism for integrating container builds into a CI workflow.

Question 9

An ACR Task must access a protected Azure resource. The organization doesn’t want credentials embedded in the task definition.

Which approach provides the most appropriate Azure-native solution?

A. Store the credential in the Dockerfile

B. Put the password in the task’s command line

C. Use a managed identity for the ACR Task

D. Make the Azure resource publicly accessible

Answer: C

Explanation: ACR Tasks can use system-assigned or user-assigned managed identities to access protected resources without embedding credentials in task definitions. The identity must be granted the required permissions on the target resource.

Question 10

A developer executes:

			
az acr build \
    --registry myregistry \
    --image orders-api:v2 \
    .

What does the final . represent?

A. The ACR registry name

B. The image tag

C. The Docker image digest

D. The build context

Answer: D

Explanation: The final . specifies the current directory as the build context. Files in the build context are made available to the Docker build process. The Dockerfile and files referenced during the build generally need to be available through the selected context.

Key Takeaways

For the AI-200 exam, remember these relationships:

Concept	Remember
ACR	Stores and manages container images
ACR Tasks	Builds, runs, tests, and automates container workflows
`az acr build`	Performs an on-demand cloud image build
`az acr run`	Runs an ACR Tasks workflow/command
Build context	Files supplied to the image build
`build`	Creates a container image
`cmd`	Runs a container
`push`	Publishes an image to a registry
`when`	Controls task-step dependencies
`when: ["-"]`	Allows an independent step to start immediately
`$ID`	Current task run identifier
`$Registry`	Registry associated with the task
Multi-step task	Build/test/run/push workflows
Source trigger	Run when source code changes
Base-image trigger	Rebuild when a base image changes
Scheduled trigger	Run according to a schedule
Managed identity	Secure access without embedding credentials

The single most useful mental model for this objective is:

                  ACR TASKS
                      |
       +--------------+--------------+
       |              |              |
     BUILD           CMD            PUSH
       |              |              |
   Create image    Run/test       Publish image
       |              |              |
       +--------------+--------------+
                      |
                      v
                     ACR

And when the exam gives you a scenario, ask:

Do I need to build an image in Azure? → az acr build / ACR Tasks
Do I need multiple build/test/run operations? → Multi-step ACR Task
Do I need to execute a container? → cmd
Do I need to publish an image? → push
Do steps have dependencies? → when
Do independent steps need to run concurrently? → when: ["-"]
Should builds happen automatically after source changes? → Source trigger
Should images rebuild when a base image changes? → Base-image trigger
Does the task need secure access to another Azure resource? → Managed identity
Do I need unique image versions for task runs? → Use $ID in the image tag

These distinctions are especially important because AI-200 scenario questions are likely to test which ACR Tasks capability best fits a particular development or deployment requirement, rather than simply asking you to recall an individual command.

Go to the AI-200 Exam Prep Hub main page

AI-200, azure, Cloud computing, Microsoft Certification August 15, 2026August 15, 2026

Build, Store, Version, and Manage Container Images by Using Azure Container Registry (AI-200 Exam Prep)

This post is a part of the AI-200: Developing AI Cloud Solutions on Azure  Exam Prep Hub.
This topic falls under these sections:
Develop containerized solutions on Azure (20–25%)
   --> Implement container application hosting
      --> Build, store, version, and manage container images by using Azure Container Registry

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Azure Container Registry (ACR) is a managed, private registry service in Azure for storing and managing container images and other OCI-compatible artifacts. It provides a central location from which containerized applications can obtain the images they need to run on services such as Azure App Service, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Container Instances.

For the AI-200: Developing AI Cloud Solutions on Azure exam, you should understand more than simply how to push an image into ACR. You should be comfortable with the hierarchy of registries, repositories, images, tags, manifests, and layers; image versioning strategies; building images using ACR Tasks; managing and deleting images; and selecting appropriate authentication and registry capabilities.

The current Microsoft Learn study guide specifically identifies this objective as part of Implement container application hosting. It also separately identifies ACR Tasks as an exam objective, so understanding how ACR stores images and how ACR Tasks builds them is particularly important.

1. What Is Azure Container Registry?

Azure Container Registry is a private container registry service hosted in Azure.

A container registry solves a fundamental problem in containerized application development:

Where do applications securely obtain the container images they need to run?

Instead of relying exclusively on a public registry, an organization can maintain its own private registry in Azure.

A typical workflow looks like this:

			
Developer
    |
    | Build container image
    v
Docker / ACR Tasks
    |
    | Push
    v
Azure Container Registry
    |
    +------------------+
    |                  |
    v                  v
Azure App Service     AKS
    |                  |
    v                  v
Container Apps       Container workloads

		

ACR provides capabilities for:

Storing container images
Storing OCI artifacts
Organizing images into repositories
Tagging and versioning images
Pushing and pulling images
Building images using ACR Tasks
Managing image metadata
Controlling access
Replicating images across regions
Integrating with Azure container services

Microsoft describes ACR as a private, managed registry that supports building, storing, and managing images for container deployments.

2. Understand the ACR Hierarchy

One of the most important concepts for AI-200 is understanding how ACR organizes container content.

The hierarchy can be thought of as:

			
Azure Container Registry
        |
        +-- Repository
        |      |
        |      +-- Image : Tag
        |      +-- Image : Tag
        |      +-- Image : Tag
        |
        +-- Repository
               |
               +-- Image : Tag
               +-- Image : Tag

		

The important concepts are:

Registry
Repository
Artifact/Image
Tag
Manifest
Layer
Digest

Understanding the distinctions between these concepts is a common source of exam questions.

2.1 Registry

The registry is the overall ACR resource.

For example:

contosoregistry.azurecr.io

The registry provides the endpoint through which clients push and pull container images.

A registry can contain many repositories.

2.2 Repository

A repository is a collection of related container images or artifacts.

For example:

contosoregistry.azurecr.io/customer-api

The repository could contain:

			
customer-api:v1
customer-api:v2
customer-api:v3

Repositories can also use namespaces:

contosoregistry.azurecr.io/marketing/campaign-api:v2

Namespaces help organize repositories logically, although they aren’t independent Azure resources or hierarchical security boundaries simply because they contain / characters.

Microsoft notes that repository names can include namespaces and are managed independently by the registry.

3. Container Image Tags

A tag provides a human-readable identifier for a particular version or variant of an image.

For example:

			
customer-api:v1
customer-api:v2
customer-api:2026-08-07
customer-api:production

The complete image reference might be:

contosoregistry.azurecr.io/customer-api:v2

The structure is:

<registry>/<repository>:<tag>

For example:

contosoregistry.azurecr.io/customer-api:v2

where:

Component	Value
Registry	`contosoregistry.azurecr.io`
Repository	`customer-api`
Tag	`v2`

Microsoft recommends using appropriate tagging strategies for deployment scenarios and notes that latest is used by default when no tag is specified in Docker commands.

4. Tagging and Versioning Strategies

Image versioning is extremely important for reliable deployments.

Consider:

customer-api:latest

This tag is convenient, but it does not necessarily identify an immutable version.

Suppose today’s latest points to:

Image A

and tomorrow the same tag is updated:

latest → Image B

A deployment configured to use latest may therefore receive a different image without its configuration changing.

For production deployments, a better approach is generally to use unique version identifiers.

Examples:

			
customer-api:v1.0.0
customer-api:v1.1.0
customer-api:v1.2.0

or:

			
customer-api:20260807.1
customer-api:20260807.2

or a source-control commit identifier:

customer-api:a81f42c

A useful pattern is:

			
latest       → convenient development/testing reference
v1.4.2       → human-readable release
a81f42c      → unique build identifier

Exam Tip

If a question asks how to ensure that a deployment consistently uses a specific image version, be cautious about answers using:

:latest

A unique tag or, even more strongly, an image digest provides better version specificity.

5. Image Digests

Container images are also identified by a digest.

For example:

contosoregistry.azurecr.io/customer-api@sha256:abc123...

A digest identifies the content associated with a manifest.

Compare:

customer-api:v2

with:

customer-api@sha256:abc123...

A tag can be moved to point to another image.

A digest identifies a specific content-addressed version.

Microsoft specifically notes that pulling by digest guarantees the image version being retrieved even if an identically tagged image is subsequently pushed.

Exam Tip

Remember:

Tag = human-friendly version reference

Digest = content-addressed, precise image reference

6. Container Image Layers

Container images consist of one or more layers.

Dockerfiles commonly create multiple layers.

For example:

			
FROM python:3.12
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY app.py .

The resulting image is composed of layers.

One of the advantages of layers is reuse.

If two images share the same base layers, those layers don’t necessarily have to be independently stored and transferred each time.

This can reduce storage and transfer requirements.

7. Manifests

A container image is associated with a manifest.

The manifest contains information needed to identify the image and its layers.

Conceptually:

			
Image Manifest
      |
      +-- Configuration
      |
      +-- Layer 1
      +-- Layer 2
      +-- Layer 3

		

The manifest is also associated with the image’s digest.

This distinction matters when managing images in ACR.

For example, removing a tag doesn’t necessarily mean that all image data is immediately removed.

An untagged manifest and its associated layers may continue to consume storage until they are deleted and no longer referenced.

Microsoft specifically warns that repeatedly pushing modified artifacts with identical tags can create untagged artifacts that continue consuming registry storage.

8. Pushing an Image to ACR

A common workflow is:

Step 1: Build the image

docker build -t customer-api:v1 .

Step 2: Tag the image with the ACR login server

			
docker tag customer-api:v1 \
    contosoregistry.azurecr.io/customer-api:v1

Step 3: Authenticate to ACR

az acr login --name contosoregistry

Step 4: Push the image

			
docker push \
    contosoregistry.azurecr.io/customer-api:v1

The image is now stored in:

contosoregistry.azurecr.io/customer-api

with the tag:

v1

9. Pulling an Image

A client can pull the image by tag:

			
docker pull \
    contosoregistry.azurecr.io/customer-api:v1

Or by digest:

			
docker pull \
    contosoregistry.azurecr.io/customer-api@sha256:<digest>

The second approach provides stronger guarantees regarding exactly which image content is retrieved.

10. Azure Container Registry Tasks

One of the most important ACR features for AI-200 is ACR Tasks.

ACR Tasks allows container images to be built in Azure rather than requiring the developer to perform the build locally.

Microsoft describes ACR Tasks as a suite of capabilities for building, testing, and managing container images.

For example:

			
az acr build \
    --registry contosoregistry \
    --image customer-api:v1 \
    --file Dockerfile .

The command:

Sends the build context to Azure.
Uses the Dockerfile.
Builds the image in Azure.
Tags the resulting image.
Pushes the resulting image into the registry.

This is particularly useful when a developer doesn’t have Docker installed locally.

Microsoft’s current quickstart explicitly demonstrates building, pushing, and running an image using ACR Tasks without requiring a local Docker installation.

11. ACR Tasks Quick Tasks

A quick task is useful for an on-demand image build.

For example:

			
az acr build \
    --registry contosoregistry \
    --image customer-api:v1 \
    .

This is useful during the development inner loop.

Instead of:

			
Developer machine
    |
    +-- docker build
    +-- docker tag
    +-- docker push

		

you can use:

			
Developer
    |
    | az acr build
    v
Azure
    |
    +-- Build
    +-- Tag
    +-- Push
    v
ACR

		

12. Automated ACR Tasks

ACR Tasks can also be configured to automatically execute when certain events occur.

For example:

			
Git commit
     |
     v
ACR Task
     |
     +-- Build image
     +-- Test image
     +-- Push image

		

ACR Tasks can also respond to base image updates.

For example, suppose an application uses:

FROM python:3.12

A base-image update can trigger an ACR Task to rebuild the application image.

This is useful for keeping application images current when their base images change.

Microsoft documents ACR Tasks triggers for Git commits and base-image updates.

13. Multi-Step ACR Tasks

ACR Tasks can execute more sophisticated workflows.

For example:

			
Build application image
        |
        v
Run application
        |
        v
Run test container
        |
        v
Push image

		

Multi-step tasks are defined using YAML.

A simplified example is:

			
version: v1.1.0
steps:
  - build: -t $Registry/customer-api:$ID .
  - push:
      - $Registry/customer-api:$ID
  - cmd: $Registry/customer-api:$ID

		

ACR Tasks supports three major step types:

Step	Purpose
`build`	Build a container image
`push`	Push an image to a registry
`cmd`	Run a container as a command

Exam Tip

If a question describes a workflow that needs to build, test, and push multiple containers, think:

ACR Tasks multi-step task

14. ACR Tasks and External Registries

ACR Tasks can also interact with other registries.

For example, a task may need to:

			
ACR
 |
 +-- Pull base image from another registry
 |
 +-- Build application
 |
 +-- Push application image to ACR

		

Credentials can be configured for tasks when access to another registry is required.

For more secure scenarios, ACR Tasks can use managed identities to access protected Azure resources without embedding credentials directly in task definitions.

15. Authentication to Azure Container Registry

ACR is generally private, so clients need appropriate authentication and authorization to access it.

Common authentication approaches include:

Microsoft Entra identities
Managed identities
Service principals
Administrator credentials
Repository-scoped access mechanisms
Anonymous pull, where explicitly configured and supported

Microsoft’s documentation emphasizes that ACR operations such as push and pull require authentication unless anonymous pull is enabled.

16. Managed Identity and ACR

Managed identities are particularly important in Azure-native applications.

Suppose an AKS cluster needs to pull an image:

			
AKS
 |
 | Managed Identity
 v
Azure Container Registry
 |
 v
customer-api:v1

		

Rather than storing a registry password in application configuration, the Azure resource can use a managed identity and appropriate permissions.

For a non-ABAC-enabled registry, a common pull-only role is:

AcrPull

For push and pull:

AcrPush

For ABAC-enabled registries, Microsoft documents repository-scoped roles such as:

			
Container Registry Repository Reader
Container Registry Repository Writer

The exact role depends on the registry’s authorization model.

Exam Tip

When the question says:

“An Azure service needs to pull images from ACR without storing credentials.”

Think:

Managed identity + appropriate ACR permissions

17. ACR Pricing Tiers

Azure Container Registry currently provides three pricing tiers:

Basic
Standard
Premium

The tiers provide increasing capacity and capabilities.

Capability	Basic	Standard	Premium
Intended use	Lower-volume scenarios	Production scenarios	High-volume/advanced scenarios
Included storage	10 GiB	100 GiB	500 GiB
Geo-replication	No	No	Yes
Private endpoints	No	No	Yes
Content trust	No	No	Yes
Customer-managed keys	No	No	Yes
Dedicated Tasks agent pools	No	No	Yes
Higher throughput/concurrency	Lower	Medium	Higher

All three tiers provide core registry capabilities, while Premium adds advanced capabilities and higher limits.

Important Exam Distinction

If the requirement is:

“Replicate a registry across multiple Azure regions.”

Think:

Premium ACR

Geo-replication is a Premium feature.

18. Geo-Replication

Geo-replication allows an ACR to replicate its content across multiple Azure regions.

For example:

                 Azure Container Registry
                          |
             +------------+------------+
             |                         |
             v                         v
         East US                  West Europe
        Geo-replica               Geo-replica
             |                         |
             v                         v
          AKS US                  AKS Europe

When an image is pushed to the geo-replicated registry, its content is synchronized to the configured replicas.

The advantage is that applications can access images from regions closer to where they run.

Microsoft describes geo-replication as providing a single registry management experience while synchronizing content across selected regions.

Don’t confuse:

Availability zones and geo-replication.

Availability zones provide resilience across zones within a region.

Geo-replication distributes registry content across different Azure regions.

Current Microsoft documentation states that zone redundancy is enabled by default for ACR registries in supported regions across Basic, Standard, and Premium tiers.

19. Managing Images and Repositories

You can manage repositories and images through:

Azure portal
Azure CLI
REST APIs
SDKs
Docker/OCI tooling

For example, you can list repositories:

			
az acr repository list \
    --name contosoregistry \
    --output table

List tags:

			
az acr repository show-tags \
    --name contosoregistry \
    --repository customer-api \
    --output table

You can also inspect manifests and image metadata.

The Azure portal exposes repositories and their image tags through the registry’s Repositories interface.

20. Deleting Images

Suppose a repository contains:

			
customer-api:v1
customer-api:v2
customer-api:v3

You can remove an image tag using Azure CLI.

For example:

			
az acr repository untag \
    --name contosoregistry \
    --image customer-api:v1

However, remember an important distinction:

Untagging an image does not necessarily immediately remove the underlying image data.

The manifest may become untagged while its layers continue consuming storage.

Microsoft specifically warns about the accumulation of untagged artifacts when images are repeatedly pushed using the same tags.

21. Retention of Untagged Manifests

ACR supports a retention policy for untagged manifests.

The purpose is to automatically remove untagged manifests after a configured period.

For example:

			
Image:v1
Image:v2
Image:v3

If v2 is removed:

Image:v2 → untagged manifest

A retention policy can eventually remove the untagged manifest.

The current Microsoft documentation identifies the untagged-manifest retention policy as a Premium feature and currently documents it as a preview feature. The policy can be configured for a retention period from 0 through 365 days.

Important Warning

If an application relies on pulling an image by its digest, automatically deleting untagged manifests can make that image unavailable.

This is an important operational consideration and a potential exam scenario.

22. Image Tagging Best Practices

A strong production tagging strategy should make image identification predictable.

A useful approach is to use multiple tags for different purposes.

For example:

			
customer-api:v2.4.1
customer-api:build-1847
customer-api:a81f42c

You might also maintain:

customer-api:production

as a deployment-oriented alias.

However, don’t rely on a mutable tag such as production or latest when you require immutable deployment behavior.

A good pattern is:

			
Human-readable release
        +
Unique build identifier
        +
Optional environment alias

		

For example:

			
customer-api:v2.4.1
customer-api:build-1847
customer-api:production

The production deployment can ultimately be pinned to a specific immutable image reference/digest.

23. Common ACR Mistakes

Mistake 1: Using `latest` for production deployments

latest can change.

Better: use unique version tags and/or digests.

Mistake 2: Assuming deleting a tag deletes the image immediately

An untagged manifest may continue consuming storage.

Better: understand manifests, layers, untagging, deletion, and retention.

Mistake 3: Giving every workload push permissions

An application that only needs to run an image generally doesn’t need permission to push images.

Better: follow least privilege.

For example:

			
Application → AcrPull
Build pipeline → AcrPush

Mistake 4: Storing registry passwords in application code

This creates unnecessary credential-management risks.

Better: use managed identities or another appropriate identity mechanism.

Mistake 5: Choosing Premium solely because it sounds better

Premium should be selected because its capabilities are required.

Examples include:

Geo-replication
Private endpoints
Content trust
Higher throughput
Advanced networking
Dedicated Tasks agent pools

Mistake 6: Confusing ACR with ACR Tasks

They are related but different concepts.

ACR:

Stores and manages container images.

ACR Tasks:

Builds, tests, and automates container image workflows.

A single ACR resource can therefore be used to store images while ACR Tasks provides the automation to build those images.

24. Important AI-200 Concepts to Know

For this exam objective, make sure you can explain the following without referring to documentation:

Concept	What you should know
Azure Container Registry	Managed private container registry
Registry	Top-level ACR resource
Repository	Collection of related images/artifacts
Tag	Human-readable image/version reference
Digest	Content-addressed image reference
Manifest	Describes image/artifact and its layers
Layer	Component of a container image
`az acr login`	Authenticates a client to ACR
`docker push`	Uploads an image to ACR
`docker pull`	Downloads an image from ACR
`az acr build`	Builds an image using ACR Tasks
ACR Tasks	Cloud-based image build/test automation
Multi-step task	Build/test/push workflows using YAML
`AcrPull`	Pull permission for applicable non-ABAC registry scenarios
`AcrPush`	Push/pull permission for applicable non-ABAC registry scenarios
Managed identity	Credential-free Azure resource authentication
Basic	Entry-level ACR tier
Standard	Higher capacity production-oriented tier
Premium	Advanced capabilities such as geo-replication/private endpoints
Geo-replication	Replicate registry content across regions
Retention policy	Automatically remove eligible untagged manifests

25. AI-200 Scenario Patterns to Recognize

The exam is likely to test your ability to choose the appropriate Azure capability based on a scenario.

Scenario: Build without Docker locally

Requirement: Developers don’t have Docker installed.

Answer: ACR Tasks / az acr build.

Scenario: Automatically rebuild after a Git commit

Requirement: Every source-code commit should trigger an image build.

Answer: ACR Task with a source-code trigger.

Scenario: Rebuild after base image updates

Requirement: Automatically rebuild application images when their base image changes.

Answer: ACR Tasks base-image trigger.

Scenario: Run the same image in several Azure regions

Requirement: Applications in multiple regions should access registry content efficiently.

Answer: ACR Premium with geo-replication.

Scenario: Application only needs to pull images

Requirement: A workload should retrieve images but shouldn’t be able to modify them.

Answer: Grant an appropriate pull-only role, such as AcrPull where applicable, or the appropriate repository reader role for an ABAC-enabled registry.

Scenario: Avoid credentials in application configuration

Requirement: An Azure-hosted application needs to access ACR without storing passwords.

Answer: Managed identity + appropriate registry permissions.

Scenario: Guarantee a specific image

Requirement: A deployment must always retrieve exactly the same image content.

Answer: Use an image digest rather than relying solely on a mutable tag.

26. Quick Review

The following mental model is useful for the exam:

                    AZURE CONTAINER REGISTRY
                             |
             +---------------+---------------+
             |                               |
        Repositories                    ACR Tasks
             |                               |
      +------+------+                  Build/Test/Push
      |             |
   Image          Image
      |             |
    Tags          Tags
      |             |
   Manifest      Manifest
      |
    Layers

And remember the major distinction:

			
ACR
 ↓
Store/manage images
ACR Tasks
 ↓
Build/test/automate images

		

For production deployments:

			
Avoid:
    :latest
Prefer:
    :v2.4.1
    :build-1847
    @sha256:<digest>

		

For authentication:

			
Azure workload
      |
      | Managed Identity
      v
     ACR
      |
      | Appropriate least-privilege role
      v
    Pull image

		

For global deployments:

			
ACR Premium
     |
     +---- Region 1
     |
     +---- Region 2
     |
     +---- Region 3

		

Practice Exam Questions

Question 1

A development team has a Dockerfile and wants to build a container image directly in Azure. Developers should not need Docker installed on their local computers. The resulting image should be pushed to Azure Container Registry.

Which Azure capability should you use?

A. Azure Container Registry Tasks

B. Azure App Service deployment slots

C. Azure Container Apps revisions

D. Azure Kubernetes Service Jobs

Answer: A

Explanation: Azure Container Registry Tasks can build container images in Azure using a Dockerfile. The az acr build command provides an on-demand build capability and can push the resulting image to ACR. A local Docker installation isn’t required for this workflow.

Question 2

An application image is stored as:

contosoregistry.azurecr.io/orders:v4

What does v4 represent?

A. The registry name

B. The image tag

C. The image digest

D. The repository namespace

Answer: B

Explanation: In an image reference such as:

registry/repository:tag

the portion after the colon is the tag. Therefore, v4 is the image tag. Tags are commonly used to identify image versions.

Question 3

A production application must always retrieve exactly the same container image content. Developers are concerned that a tag could later be reassigned to a different image.

Which image reference should the application use?

A. :latest

B. :production

C. :stable

D. @sha256:<digest>

Answer: D

Explanation: Tags can be moved to different image versions. A digest is a content-addressed identifier and can be used to pull a specific image version. Microsoft specifically identifies digest-based pulls as a way to guarantee the image version being retrieved.

Question 4

An organization deploys applications to Azure regions in North America and Europe. The organization wants container images to be replicated to both regions while maintaining a single ACR management experience.

Which ACR capability should be used?

A. Repository namespaces

B. Availability zones

C. Geo-replication

D. Image tags

Answer: C

Explanation: ACR geo-replication synchronizes registry content across selected Azure regions while allowing the organization to manage the registry as a single logical registry. Geo-replication is a Premium ACR capability.

Question 5

An AKS workload needs to pull private container images from ACR. The organization does not want to store registry passwords in Kubernetes configuration.

Which approach is most appropriate?

A. Use a managed identity with appropriate ACR permissions

B. Store the ACR administrator password in the container image

C. Make the repository publicly accessible

D. Embed an ACR password in the application source code

Answer: A

Explanation: Azure resources can use managed identities to authenticate to ACR without storing credentials in application code or configuration. The identity must be granted the appropriate pull permissions.

Question 6

A development team wants an automated container workflow that performs the following:

Builds an application image.
Runs a test container.
Builds another image.
Pushes the resulting images.

Which ACR capability should the team use?

A. ACR repository namespaces

B. ACR multi-step Tasks

C. ACR geo-replication

D. ACR anonymous pull

Answer: B

Explanation: ACR Tasks supports multi-step workflows using YAML. The workflow can build, run/test, and push one or more images. The available step types include build, push, and cmd.

Question 7

An organization repeatedly pushes new builds using the same image tag. After several months, the registry contains significant amounts of storage that cannot be explained by the currently tagged images.

What is the most likely explanation?

A. ACR automatically creates a new repository for every push

B. Geo-replication is duplicating every image within the same region

C. Previous manifests became untagged while their image data remained in the registry

D. ACR stores every Dockerfile indefinitely

Answer: C

Explanation: Repeatedly pushing modified images using the same tag can result in previous manifests becoming untagged. Their layers can continue consuming registry storage until the underlying content is deleted.

Question 8

A company needs to automatically rebuild its application container whenever a new version of the application’s base container image becomes available.

Which capability should be configured?

A. Azure App Service deployment slots

B. ACR geo-replication

C. ACR repository tagging

D. An ACR Task with a base-image update trigger

Answer: D

Explanation: ACR Tasks can automatically trigger builds when a base image is updated. This is useful for rebuilding application images when their underlying base images change.

Question 9

An organization requires private connectivity to its Azure Container Registry through Azure Private Link. Which ACR pricing tier supports this capability?

A. Premium

B. Basic

C. Standard

D. All three tiers

Answer: A

Explanation: Azure Container Registry Premium supports private endpoints through Private Link. Basic and Standard do not provide this capability according to the current ACR SKU documentation.

Question 10

An administrator removes the v1 tag from an image in an ACR repository. The administrator assumes that the underlying image data has immediately been removed from the registry.

Which statement is correct?

A. Removing a tag always immediately deletes every associated layer

B. Removing a tag converts the image automatically into a public image

C. Removing a tag deletes the entire repository

D. The manifest can become untagged while its data continues consuming storage

Answer: D

Explanation: Removing a tag can leave the manifest untagged while its associated data remains in the registry. Untagged artifacts can continue consuming storage until they are deleted. ACR provides mechanisms such as retention policies for eligible untagged manifests.

Final AI-200 Takeaways

For this particular AI-200 objective, concentrate on these distinctions:

Azure Container Registry

Store and manage container images and artifacts.

ACR repository

Organizes related images.

Tag

Human-readable version/reference that can be reassigned.

Digest

Content-addressed identifier for a specific image version.

Manifest

Describes the image/artifact and its layers.

ACR Tasks

Build, test, and automate container image workflows.

az acr build

Perform an on-demand cloud-based container build.

Multi-step ACR Task

Build/test/push multiple images or perform multi-stage workflows.

Managed identity

Authenticate Azure workloads to ACR without managing passwords.

AcrPull

Pull permission for applicable non-ABAC registry scenarios.

AcrPush

Push/pull permission for applicable non-ABAC registry scenarios.

Premium

Required for capabilities such as geo-replication and private endpoints.

Geo-replication

Replicate registry content across Azure regions.

Retention

Help clean up eligible untagged manifests.

The most important exam mindset is to distinguish where the image is stored, how it is identified, how it is built, and how the workload is authorized to retrieve it. Those four dimensions—registry/repository, tag/digest, ACR Tasks, and authentication/RBAC—cover a large portion of the practical knowledge behind this objective.

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Overview

1. Understanding Kubernetes Manifest Files

2. YAML Manifest Structure

3. The apiVersion Property

4. The kind Property

5. Kubernetes Deployments

6. Labels and Selectors

Exam Tip

7. Container Images

Best Practice

8. Connecting AKS to Azure Container Registry

9. Exposing an Application with a Service

10. Service Types

ClusterIP

NodePort

LoadBalancer

11. Deploying a Manifest to AKS

12. Applying Multiple Resources in One File

13. Connecting kubectl to AKS

Exam Tip

14. Namespaces

15. Environment Variables

16. ConfigMaps

17. Kubernetes Secrets

18. Resource Requests and Limits

Requests

Limits

19. Health Probes

Startup probe

Readiness probe

Liveness probe

Exam Distinction

20. Updating an Application

21. Checking Deployment Status

22. Viewing Application Logs

23. Using kubectl describe

24. Common Deployment Problems

ImagePullBackOff

CrashLoopBackOff

Pod stuck in Pending

Service has no endpoints

25. Managing Applications Declaratively

26. Manifest Files and CI/CD

27. Example Complete Manifest

28. Important Commands to Know

29. Key Exam Takeaways

Manifest

Deployment

Pod

Service

Labels

Selectors

ConfigMap

Secret

kubectl apply

ACR

Readiness probe

Liveness probe

Startup probe

kubectl describe

kubectl logs

Practice Exam Questions

Question 1

Answer: A

Explanation

Question 2

Answer: B

Explanation

Question 3

Answer: C

Explanation

Question 4

Answer: B

Explanation

Question 5

Answer: A

Explanation

Question 6

Answer: D

Explanation

3. The `apiVersion` Property

4. The `kind` Property

13. Connecting `kubectl` to AKS

23. Using `kubectl describe`

`ImagePullBackOff`

`CrashLoopBackOff`

Pod stuck in `Pending`

`kubectl apply`

`kubectl describe`

`kubectl logs`

23. Using `latest` vs. Versioned Tags

`latest`