Tag: AI-200: Developing AI Cloud Solutions on Azure

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:

  1. Registry
  2. Repository
  3. Artifact/Image
  4. Tag
  5. Manifest
  6. Layer
  7. 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:

ComponentValue
Registrycontosoregistry.azurecr.io
Repositorycustomer-api
Tagv2

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:

  1. Sends the build context to Azure.
  2. Uses the Dockerfile.
  3. Builds the image in Azure.
  4. Tags the resulting image.
  5. 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:

StepPurpose
buildBuild a container image
pushPush an image to a registry
cmdRun 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.

CapabilityBasicStandardPremium
Intended useLower-volume scenariosProduction scenariosHigh-volume/advanced scenarios
Included storage10 GiB100 GiB500 GiB
Geo-replicationNoNoYes
Private endpointsNoNoYes
Content trustNoNoYes
Customer-managed keysNoNoYes
Dedicated Tasks agent poolsNoNoYes
Higher throughput/concurrencyLowerMediumHigher

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:

ConceptWhat you should know
Azure Container RegistryManaged private container registry
RegistryTop-level ACR resource
RepositoryCollection of related images/artifacts
TagHuman-readable image/version reference
DigestContent-addressed image reference
ManifestDescribes image/artifact and its layers
LayerComponent of a container image
az acr loginAuthenticates a client to ACR
docker pushUploads an image to ACR
docker pullDownloads an image from ACR
az acr buildBuilds an image using ACR Tasks
ACR TasksCloud-based image build/test automation
Multi-step taskBuild/test/push workflows using YAML
AcrPullPull permission for applicable non-ABAC registry scenarios
AcrPushPush/pull permission for applicable non-ABAC registry scenarios
Managed identityCredential-free Azure resource authentication
BasicEntry-level ACR tier
StandardHigher capacity production-oriented tier
PremiumAdvanced capabilities such as geo-replication/private endpoints
Geo-replicationReplicate registry content across regions
Retention policyAutomatically 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:

  1. Builds an application image.
  2. Runs a test container.
  3. Builds another image.
  4. 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.


Go to the AI-200 Exam Prep Hub main page

Implement a change feed processor to detect and handle new or updated items (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 AI solutions by using Azure data management services (25–30%)
   --> Develop AI solutions by using Azure Cosmos DB for NoSQL
      --> Implement a change feed processor to detect and handle new or updated items


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 Cosmos DB for NoSQL provides a change feed that records changes made to items in a container. Applications can consume this feed to react to data changes without repeatedly querying the entire container.

For the AI-200 exam, an important implementation pattern is the change feed processor. It provides a push-based mechanism for detecting changes and delivering them to application code for processing.

A change feed processor is particularly useful when an application needs to perform an action whenever items are created or updated, such as:

  • Processing newly submitted documents
  • Generating embeddings for newly created content
  • Updating a search index
  • Synchronizing data with another system
  • Running AI processing when new data arrives
  • Performing analytics or enrichment
  • Triggering downstream business workflows
  • Maintaining materialized or derived data

The change feed processor also handles important operational concerns such as checkpointing, load balancing, lease management, and recovery.


1. What Is the Azure Cosmos DB Change Feed?

The change feed is a persistent record of changes to items in an Azure Cosmos DB container.

Conceptually, it looks like this:

Application
|
| Creates/updates items
v
Azure Cosmos DB Container
|
| Change feed
v
Change Feed Processor
|
+--> Process new item
+--> Generate embedding
+--> Update search index
+--> Call downstream service
+--> Store derived data

Instead of repeatedly asking:

“Which items have changed since the last time I checked?”

the application can consume the change feed and process changes incrementally.

This makes the change feed especially useful for event-driven and near-real-time architectures.


2. Latest Version Change Feed Mode

For the AI-200 scenario involving detection of new or updated items, the default latest version change feed mode is particularly important.

In latest version mode:

  • Creates appear in the change feed.
  • Updates appear in the change feed.
  • Deletes do not appear.
  • If an item is changed multiple times before it is read, the feed provides the latest version rather than every intermediate version.

For example:

Item created
|
v
Status = "Pending"
|
v
Status = "Processing"
|
v
Status = "Completed"

If these changes occur before the consumer reads the feed, latest-version mode may expose the current version rather than every intermediate state.

Therefore, latest-version mode is appropriate when the application cares about the current state of changed items, rather than every individual mutation.

Important exam distinction

If an application must detect deletes or process every intermediate version, latest-version mode isn’t sufficient.

Azure Cosmos DB also supports all versions and deletes mode, which captures creates, updates, and deletes. That mode has additional requirements, including continuous backup, and is available for Azure Cosmos DB for NoSQL.


3. What Is a Change Feed Processor?

The change feed processor is a higher-level mechanism for consuming the Azure Cosmos DB change feed.

It uses a push model.

Rather than requiring your application to repeatedly pull batches and manage continuation state itself, the processor:

  1. Reads changes from the monitored container.
  2. Determines which changes need to be processed.
  3. Delivers batches of changes to your application code.
  4. Maintains processing state using a lease container.
  5. Distributes work among multiple processor instances.
  6. Recovers work when an instance fails.

The change feed processor is currently provided through the Azure Cosmos DB .NET V3 and Java V4 SDKs. Python and Node.js applications can consume the change feed using the pull model rather than the change feed processor library.


4. The Four Components of a Change Feed Processor

A key AI-200 concept is understanding the four major components.

4.1 Monitored Container

The monitored container is the Azure Cosmos DB container whose changes you want to process.

For example:

Database: AIApplication
Container: Documents
Partition key: /customerId

The processor monitors Documents.

When items are created or updated, those changes become available through the change feed.


4.2 Lease Container

The lease container stores the state used by the change feed processor to coordinate processing.

This is extremely important.

The lease container allows multiple processor instances to share the workload without processing the same lease simultaneously.

Conceptually:

                 Lease Container
                /       |       \
               /        |        \
              v         v         v
          Lease 1    Lease 2    Lease 3
             |          |          |
             v          v          v
          Worker A   Worker B   Worker C

The leases represent ownership and progress for portions of the change feed.

The lease container can be in the same Cosmos DB account as the monitored container or in a separate account.

Exam tip

If a question asks:

What component maintains the state of change feed processing?

The answer is generally:

The lease container.


5. Compute Instances

A compute instance hosts the change feed processor.

Examples include:

  • Azure Kubernetes Service pods
  • Azure App Service instances
  • Azure Virtual Machines
  • Long-running application processes
  • Hosted background services

For example:

AKS Cluster
Pod 1 --> Change Feed Processor
Pod 2 --> Change Feed Processor
Pod 3 --> Change Feed Processor

Each processor instance must have a unique instance name.

The processor distributes leases among the available instances.


6. The Delegate

The delegate is your application code that processes the changes.

For example, suppose an AI application stores documents in Cosmos DB.

When a document changes, the delegate might:

  1. Extract the text.
  2. Generate an embedding.
  3. Store the embedding.
  4. Update a vector index.
  5. Record processing status.

Conceptually:

Cosmos DB Change
|
v
Change Feed Processor
|
v
Delegate
|
+--> Extract text
|
+--> Generate embedding
|
+--> Store embedding
|
+--> Update AI search data

The delegate is therefore where the application’s business logic lives.


7. How the Processing Lifecycle Works

The basic lifecycle is:

Read change feed
|
v
Are there changes?
/ \
No Yes
| |
v v
Wait Send batch
| |
+------<-------+
|
v
Delegate succeeds?
/ \
No Yes
| |
v v
Retry from Update
checkpoint lease

More precisely, the processor:

  1. Reads the change feed.
  2. Waits if no changes are available.
  3. Sends a batch of changes to the delegate.
  4. Waits for successful processing.
  5. Updates the lease with the latest successfully processed position.
  6. Continues processing.

The checkpoint is therefore advanced after successful processing.


8. Why the Change Feed Processor Uses At-Least-Once Processing

One of the most important concepts for the exam is that the change feed processor provides an at-least-once delivery guarantee.

Suppose the processor reads:

Change A
Change B
Change C

and passes them to your delegate.

If the delegate fails before the checkpoint is successfully updated, the processor can process those changes again.

Therefore:

Change A
Change B
Change C
|
v
Process
|
X Failure
|
v
Retry
|
v
Change A
Change B
Change C

This means your application should generally be idempotent.


9. Why Idempotency Matters

An idempotent operation can safely be executed more than once without producing an incorrect final result.

For example, suppose the change feed processor receives:

{
"id": "document-123",
"status": "completed"
}

Your processing logic might update a downstream record:

document-123 -> completed

If the same change is processed twice, the final state remains:

document-123 -> completed

That is preferable to an operation such as:

balance = balance + 100

where processing the same event twice could incorrectly add the amount twice.

Exam rule

Design change feed handlers assuming a change may be delivered more than once.


10. Lease-Based Load Distribution

The change feed processor can distribute processing across multiple instances.

For example:

Change Feed
------------------------------------------------
Partition Range 1
Partition Range 2
Partition Range 3
Partition Range 4
------------------------------------------------
| | | |
v v v v
Worker 1 Worker 2 Worker 3 Worker 4

The lease container coordinates ownership of these workloads.

If one worker fails, its leases can eventually be acquired by another worker.

This provides fault tolerance without requiring the developer to manually coordinate workers.


11. Scaling the Change Feed Processor

Suppose you initially have:

Worker 1

and later add:

Worker 2
Worker 3

The change feed processor can redistribute leases among the workers.

Conceptually:

Before:
Worker 1
├── Lease 1
├── Lease 2
├── Lease 3
└── Lease 4
After scaling:
Worker 1
├── Lease 1
└── Lease 2
Worker 2
└── Lease 3
Worker 3
└── Lease 4

This allows processing to be parallelized.

However, simply adding instances does not mean that processing becomes infinitely parallel.

The available workload is constrained by the number of leases/partition ranges.

The number of processor instances should not exceed the number of available leases for meaningful distribution.


12. Partitioning and Change Feed Processing

Azure Cosmos DB containers are partitioned using a partition key.

For example:

Container: Documents
Partition key: /customerId

The change feed processor works with the underlying partition ranges.

Each range can be processed independently, allowing parallel processing.

This is one reason that selecting an appropriate partition key remains important even when using the change feed.

A poor partition key can create an uneven workload.


13. Starting Position

An important implementation detail is the processor’s starting position.

When a change feed processor is initialized for the first time, its starting point determines which changes it processes.

In latest-version mode, you can configure the processor to start from a specified time or from the beginning of the container’s lifetime.

For example:

Container history
|
|---- Change A
|---- Change B
|---- Change C
|---- Change D
|---- Change E
|
^
|
Start processor

If configured to begin at Change A, the processor can process the historical changes.

If configured to start from the current point, older changes aren’t processed.

Important

The starting-position configuration is used when initializing the processor. Once the lease container has established the processor’s state, changing the starting configuration doesn’t reset the existing checkpoint.


14. Change Feed Processor vs. Pull Model

There are two major approaches to consuming the change feed.

FeatureChange Feed ProcessorPull Model
Processing stylePushPull
Checkpoint managementLease containerApplication-managed continuation
Load balancingBuilt inApplication responsibility
Error/retry infrastructureBuilt inApplication responsibility
.NET supportYesYes
Java supportYesYes
PythonNot through processor libraryYes
Node.jsNot through processor libraryYes

The change feed processor is generally easier when you want Azure Cosmos DB to manage the mechanics of distributing work and maintaining processing state.


15. Change Feed Processor vs. Azure Functions Trigger

Another important distinction is between the change feed processor and the Azure Functions trigger for Cosmos DB.

Both can be used to build event-driven applications.

For example:

Cosmos DB
|
+----> Change Feed Processor
|
+----> Azure Functions Trigger

The change feed processor is useful when you need more direct control over a long-running processing application.

The Azure Functions trigger is useful when you want a serverless implementation.

The Azure Functions trigger also uses a lease container to maintain processing state.


16. Handling Processing Failures

Suppose your delegate encounters an exception:

Batch
|
v
Delegate
|
X Exception

The processor doesn’t simply assume the batch succeeded.

Because the checkpoint hasn’t advanced successfully, the processor can retry the batch.

This behavior produces the at-least-once guarantee.

Important design consideration

If a particular item consistently causes processing to fail, the processor can repeatedly encounter the same problem.

A robust application should therefore have an error-handling strategy.

For example:

Change
|
v
Process
|
X Failure
|
+--> Retry
|
+--> Persistent failure
|
v
Error/DLQ storage

An application might persist information about the failed change to another Cosmos DB container or another durable store so that the processing pipeline doesn’t remain permanently blocked by one problematic change.


17. Monitoring Change Feed Lag

A change feed processor can fall behind the incoming changes.

For example:

New changes:
1000 events/sec
Processing:
700 events/sec
Result:
Change feed lag increases

The change feed estimator can be used to monitor processor progress and estimate lag.

This can help identify:

  • Insufficient processing capacity
  • Slow downstream services
  • Throttling
  • Application errors
  • Lease problems
  • Processing bottlenecks

18. Request Units and the Change Feed

Change feed processing isn’t free from a Cosmos DB throughput perspective.

Reading the change feed from the monitored container consumes request units (RUs).

Operations involving the lease container also consume RUs.

For example:

Monitored Container
|
+--> Change feed reads --> RU consumption
Lease Container
|
+--> Lease reads
+--> Lease updates
+--> Lease coordination
|
v
RU consumption

If the monitored or lease container experiences throttling, change processing can be delayed.

This is especially important when deploying multiple processor instances or multiple processing workloads that share a lease container.


19. Lease Container Permissions

When Microsoft Entra ID authentication is used, the processor’s identity needs appropriate permissions.

The monitored container requires permissions related to:

  • Reading account metadata
  • Reading the change feed

The lease container requires permissions for operations such as:

  • Reading items
  • Creating items
  • Replacing items
  • Deleting items
  • Executing queries

This is an important distinction:

The application doesn’t just need permission to read the monitored data; it also needs permission to maintain the processor’s lease state.


20. Using a Global Endpoint

For a change feed processor workload, Microsoft recommends using the global Cosmos DB endpoint rather than a region-specific endpoint.

For example:

Preferred:
https://contoso.documents.azure.com

rather than:

https://contoso-westus.documents.azure.com

Regional preferences should be configured through the appropriate SDK region settings.

This is important because lease documents are scoped to the configured endpoint. Changing endpoints can result in separate lease state.


21. A Typical AI Application Architecture

Consider an AI document-processing application.

A user uploads a document, and the application stores metadata in Cosmos DB.

The desired workflow is:

User
|
v
Application
|
v
Cosmos DB
|
| New/updated document
v
Change Feed
|
v
Change Feed Processor
|
v
Processing Delegate
|
+--> Extract document text
|
+--> Generate embedding
|
+--> Store vector
|
+--> Update search metadata
|
+--> Notify downstream application

This architecture avoids repeatedly scanning the entire container looking for new work.

It also allows the processing workload to scale independently from the application that writes the data.


22. Example .NET Concept

A simplified .NET implementation conceptually looks like this:

var processor = monitoredContainer
.GetChangeFeedProcessorBuilder<MyDocument>(
"documentProcessor",
HandleChangesAsync)
.WithInstanceName("worker-01")
.WithLeaseContainer(leaseContainer)
.Build();
await processor.StartAsync();

The important concepts are:

  • monitoredContainer — where changes originate.
  • leaseContainer — where processing state is maintained.
  • HandleChangesAsync — your business logic.
  • WithInstanceName — uniquely identifies the processor instance.
  • Processor startup — begins monitoring the change feed.

The exact SDK APIs can vary by SDK version, so the exam focus should be on understanding the architecture and responsibilities rather than memorizing every method signature. The current change feed processor documentation identifies .NET V3 and Java V4 as the SDKs that provide the processor library.


23. Important Exam Concepts to Remember

For AI-200, make sure you can distinguish the following:

Monitored container

Contains the data whose changes are being detected.

Lease container

Maintains processor state and coordinates work across instances.

Delegate

Contains the application’s processing logic.

Compute instance

Hosts the change feed processor.

Latest-version mode

Captures the latest versions of creates and updates; deletes aren’t included.

All versions and deletes mode

Captures creates, updates, and deletes, including intermediate changes.

Checkpoint

Records the latest successfully processed position.

At-least-once delivery

A change can be processed more than once, so handlers should be idempotent.

Pull model

The application manages reading, continuation state, and processing coordination.

Change feed processor

Provides a higher-level push-based processing model with lease-based coordination.


Practice Exam Questions

Question 1

An AI application stores documents in an Azure Cosmos DB for NoSQL container. Whenever a document is created or updated, the application must perform additional processing. The development team wants Azure Cosmos DB to manage checkpointing and distribute processing across multiple application instances.

Which solution should the team implement?

A. A timer-triggered Azure Function that scans the container

B. Periodic SQL queries

C. Azure Cosmos DB analytical store queries

D. Change feed processor

Answer: D

Explanation

The change feed processor is designed to process changes incrementally and provides built-in lease-based coordination and checkpoint management. It can distribute change feed processing across multiple instances.

The other approaches require the application to identify changes itself and are less appropriate for event-driven incremental processing.


Question 2

A change feed processor processes a batch of changes successfully but fails before the processing state is checkpointed. What should the application expect?

A. The changes are permanently discarded

B. The batch can be delivered again

C. The entire Cosmos DB container is automatically restored

D. The change feed is permanently disabled

Answer: B

Explanation

The change feed processor provides at-least-once delivery. If processing succeeds but the checkpoint isn’t successfully advanced, the processor can process the same changes again.

Application processing logic should therefore be designed to be idempotent.


Question 3

Which component is primarily responsible for maintaining the state and coordinating ownership of change feed processing across multiple processor instances?

A. Monitored container

B. Compute instance

C. Lease container

D. Application Gateway

Answer: C

Explanation

The lease container stores the state used by the change feed processor to coordinate processing across instances.

The monitored container provides the source data, while compute instances host the processing application.


Question 4

An application uses the default latest-version change feed mode. An item is created and then updated three times before the processor reads the changes. What behavior should the application expect?

A. Only the delete operation is returned

B. All four versions are guaranteed to be returned

C. No changes are returned because the item changed multiple times

D. The latest version of the item is available rather than every intermediate version

Answer: D

Explanation

Latest-version mode provides the latest version of an item in the feed rather than preserving every intermediate change between reads.

If the application needs every create, update, and delete operation, it should consider all versions and deletes mode instead.


Question 5

A developer is building a change feed processor application that will run on three AKS pods. What is the primary purpose of assigning each processor instance a unique instance name?

A. To identify each compute instance participating in lease distribution

B. To specify the Cosmos DB partition key

C. To determine the consistency level

D. To select the Cosmos DB database

Answer: A

Explanation

Each change feed processor instance should have a unique instance name. The processor uses the instances and leases to distribute processing work across the deployment.

The instance name is unrelated to partition-key selection, database selection, or consistency configuration.


Question 6

An AI application must react when documents are deleted from an Azure Cosmos DB for NoSQL container. Which change feed capability is most appropriate?

A. Latest-version change feed mode

B. All versions and deletes change feed mode

C. Increasing the consistency level

D. Increasing the container’s RU/s

Answer: B

Explanation

All versions and deletes mode captures creates, updates, and deletes.

Latest-version mode does not capture deletes.

All versions and deletes mode has additional requirements, including continuous backup, and is specifically available for Azure Cosmos DB for NoSQL.


Question 7

A change feed processor application experiences increasingly large processing delays. Investigation shows that the application is processing changes correctly but cannot keep up with incoming changes.

Which metric or capability is most useful for determining whether the processor is falling behind?

A. Azure DNS query count

B. Azure Storage blob count

C. Change feed estimator

D. Azure Resource Manager activity log

Answer: C

Explanation

The change feed estimator can be used to estimate the lag between the changes available in the monitored container and the progress of the change feed processor.

This can help identify processing bottlenecks and determine whether additional processing capacity may be necessary.


Question 8

A change feed processor’s delegate updates an external database. The same change may occasionally be delivered more than once. What should the developer do?

A. Disable checkpointing

B. Use an idempotent processing design

C. Increase the Cosmos DB consistency level to strong

D. Disable leases

Answer: B

Explanation

The change feed processor provides at-least-once delivery, meaning a change can be processed more than once.

The delegate should therefore be designed to handle duplicate processing safely. Idempotent operations are one of the most important techniques for doing this.


Question 9

A company runs several change feed processor instances and notices that the lease container is experiencing RU throttling. What is a likely consequence?

A. Change feed processing can be delayed

B. All documents in the monitored container are deleted

C. The Cosmos DB account automatically switches to strong consistency

D. The application automatically receives unlimited RU/s

Answer: A

Explanation

The lease container performs operations that consume request units. If the lease container is throttled, lease coordination and renewal can be delayed, which can delay change feed processing.

The monitored container’s change feed reads also consume RUs. Both the monitored and lease containers should therefore be appropriately provisioned.


Question 10

A development team wants to consume an Azure Cosmos DB change feed from a Python application. They want to use the built-in change feed processor library that automatically handles lease-based processing.

What should the team do?

A. Use the .NET change feed processor library from Python

B. Use the Java change feed processor library from Python

C. Use the change feed pull model from Python

D. Use Azure SQL Database instead

Answer: C

Explanation

The Azure Cosmos DB change feed processor library is available for .NET and Java. Python applications can consume the change feed using the pull model, where the application manages continuation state and processing.


Key Takeaways

For the AI-200 exam, the most important ideas are:

  1. The change feed records changes to Azure Cosmos DB items.
  2. The change feed processor provides a push-based processing model.
  3. The monitored container is the source of changes.
  4. The lease container stores processing state and coordinates workers.
  5. The delegate contains the application’s change-processing logic.
  6. Multiple processor instances can share the workload through leases.
  7. Change feed processing provides at-least-once delivery.
  8. Handlers should therefore be idempotent.
  9. Latest-version mode captures creates and updates but not deletes.
  10. All versions and deletes mode captures creates, updates, and deletes.
  11. The change feed processor library is available for .NET and Java; Python and Node.js use the pull model.
  12. Change feed processing consumes RUs.
  13. Throttling of the monitored or lease container can delay processing.
  14. The change feed estimator can help identify processing lag.
  15. The lease container is fundamental to distributed, fault-tolerant change feed processing.

The exam’s scenario questions are likely to test whether you can select the right change feed mode, processing model, lease architecture, error-handling strategy, and scaling approach, rather than simply recognizing the term “change feed.”


Go to the AI-200 Exam Prep Hub main page

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Bookmark the hubs you are interested in and use them to ensure you are fully prepared for the respective exam.

Each hub contains:

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  2. Practice exam questions for each section.
  3. Bonus material to help you prepare
  4. Two (2) Practice Exams with 60 questions each, or Four (4) Practice Exams with 30 questions each – along with answers.
  5. Links to useful resources, such as Microsoft Learn content, YouTube video series, and more.






AI-900: Microsoft Azure AI Fundamentals

WARNING: AI-900 will retire on June 30, 2026. It will be replaced with AI-901. You can continue to earn this certification after AI-900 retires by passing AI-901.


AI-901: Microsoft Azure AI Fundamentals

AI-901 replaces AI-900.