Tag: Model Management

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification

Practice Questions: Describe Model Management and Deployment Capabilities in Azure Machine Learning (AI-900 Exam Prep)

Practice Questions

Question 1

You train multiple machine learning models using different algorithms and want to store them in a central location with version tracking. Which Azure Machine Learning capability should you use?

A. Azure Kubernetes Service
B. Model registration
C. Batch endpoints
D. Automated machine learning

Correct Answer: B

Explanation:
Model registration stores trained models in Azure Machine Learning, enabling centralized management, versioning, and reuse.

Question 2

Why is model versioning important in Azure Machine Learning?

A. To reduce compute costs
B. To allow rollback to previous model versions
C. To encrypt model files
D. To improve model accuracy automatically

Correct Answer: B

Explanation:
Model versioning allows teams to track changes over time and revert to earlier versions if newer models perform poorly.

Question 3

Which deployment option should you use when predictions must be returned immediately to a web application?

A. Batch endpoint
B. Training pipeline
C. Real-time endpoint
D. Experiment run

Correct Answer: C

Explanation:
Real-time endpoints provide low-latency predictions through REST APIs, making them suitable for applications that need immediate responses.

Question 4

A data science team wants to score millions of records overnight without requiring instant responses. Which deployment approach is most appropriate?

A. Real-time endpoint
B. Batch endpoint
C. Azure Functions
D. Model registration

Correct Answer: B

Explanation:
Batch endpoints are designed for large-scale, offline predictions and do not require low-latency responses.

Question 5

Which Azure Machine Learning feature tracks metrics, parameters, and outputs from training runs?

A. Model deployment
B. Experiment tracking
C. Model endpoint
D. Azure Blob Storage

Correct Answer: B

Explanation:
Experiment tracking captures training details such as metrics and parameters, enabling comparison and reproducibility.

Question 6

After training a model, what is the primary purpose of registering it in Azure Machine Learning?

A. To retrain the model automatically
B. To expose the model as an API
C. To store and manage the model for future use
D. To encrypt the dataset

Correct Answer: C

Explanation:
Registering a model allows it to be stored, versioned, and managed, making it available for deployment and reuse.

Question 7

Which Azure Machine Learning capability simplifies scaling and infrastructure management when deploying models?

A. Model versioning
B. Containerized deployment
C. Experiment tracking
D. Data labeling

Correct Answer: B

Explanation:
Azure Machine Learning packages models into containers, simplifying deployment, scaling, and infrastructure management.

Question 8

What is a key difference between real-time endpoints and batch endpoints?

A. Real-time endpoints do not require models
B. Batch endpoints are used only for training
C. Real-time endpoints provide immediate predictions
D. Batch endpoints use more accurate models

Correct Answer: C

Explanation:
Real-time endpoints return predictions immediately, while batch endpoints process large datasets asynchronously.

Question 9

Which task is part of model management rather than model deployment?

A. Exposing a REST API
B. Scaling compute resources
C. Registering and versioning models
D. Handling prediction requests

Correct Answer: C

Explanation:
Registering and versioning models are model management tasks. Deployment focuses on making models available for predictions.

Question 10

Which statement best describes Azure Machine Learning’s role in model deployment?

A. It requires manual server configuration
B. It automates model training only
C. It simplifies deploying models to scalable endpoints
D. It replaces Azure Kubernetes Service

Correct Answer: C

Explanation:
Azure Machine Learning abstracts infrastructure complexity, making it easier to deploy models as scalable endpoints.

Final Exam Tips ✅

  • Model registration = storage + versioning
  • Real-time endpoint = immediate predictions
  • Batch endpoint = large-scale, offline predictions
  • AI-900 tests concepts, not implementation details

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification

Describe Model Management and Deployment Capabilities in Azure Machine Learning (AI-900 Exam Prep)

Where this fits in the exam

  • Exam domain: Describe fundamental principles of machine learning on Azure (15–20%)
  • Sub-area: Describe Azure Machine Learning capabilities
  • Skill level: Conceptual understanding (no deep implementation details)

For AI-900, Microsoft expects you to understand what Azure Machine Learning can do for managing and deploying models — not how to write code or configure infrastructure in detail.

What Is Model Management in Azure Machine Learning?

Model management refers to how machine learning models are:

  • Stored
  • Versioned
  • Tracked
  • Prepared for deployment

Azure Machine Learning provides built-in tools to manage the entire model lifecycle, from training to production.

Key Model Management Capabilities

1. Model Registration

After a model is trained, it can be registered in Azure Machine Learning.

What model registration provides:

  • Centralized model storage
  • Model versioning
  • Metadata tracking (name, version, description)
  • Easy reuse across experiments and deployments

📌 Exam tip:
Registration allows multiple versions of the same model to be stored and compared.

2. Model Versioning

Azure Machine Learning automatically assigns versions to registered models.

Why this matters:

  • Compare performance between model versions
  • Roll back to a previous version if a newer model performs poorly
  • Support continuous improvement and experimentation

📌 AI-900 focus:
You only need to know that versioning exists and why it’s useful, not how to configure it.

3. Experiment Tracking

Azure Machine Learning tracks:

  • Training runs
  • Parameters
  • Metrics (accuracy, error, etc.)
  • Output artifacts

This helps data scientists:

  • Compare models
  • Reproduce results
  • Understand how a model was created

Model Deployment in Azure Machine Learning

Once a model is trained and registered, it can be deployed so applications can use it to make predictions.

Deployment Options in Azure Machine Learning

1. Real-Time Endpoints

Used for on-demand predictions.

Key characteristics:

  • Low-latency responses
  • Exposed via a REST API
  • Commonly used for web or application integrations

Typical compute targets:

  • Azure Kubernetes Service (AKS)
  • Azure Container Instances (ACI)

📌 Exam tip:
Real-time endpoints are used when predictions are needed immediately.

2. Batch Endpoints

Used for large-scale, offline predictions.

Key characteristics:

  • Processes large datasets at once
  • Not time-sensitive
  • Often scheduled or run periodically

Example use cases:

  • Scoring customer records overnight
  • Generating predictions for reports

Managed Deployment Features

Azure Machine Learning simplifies deployment by providing:

  • Containerized deployments
    Models are packaged into containers for consistency.
  • Scaling support
    Automatically handles increasing or decreasing load.
  • Monitoring and logging
    Tracks performance and usage after deployment.

📌 AI-900 emphasis:
You should understand that Azure ML manages infrastructure complexity, not the low-level details.

Model Management vs Deployment (At a Glance)

CapabilityPurpose
Model registrationStore and organize trained models
VersioningTrack changes and improvements
Experiment trackingCompare training runs and metrics
Real-time deploymentImmediate predictions via API
Batch deploymentLarge-scale, offline predictions

Why This Matters for AI-900

For the AI-900 exam, Microsoft wants you to recognize that:

  • Azure Machine Learning supports the full ML lifecycle
  • Models can be managed, versioned, and deployed without custom infrastructure
  • Deployment can be real-time or batch, depending on the scenario

You are not expected to:

  • Write deployment scripts
  • Configure Kubernetes clusters
  • Optimize production pipelines

Key Takeaways for the Exam

  • Azure Machine Learning provides centralized model management
  • Models can be registered and versioned
  • Deployment options include real-time endpoints and batch endpoints
  • Azure ML simplifies scaling, monitoring, and management

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Go to the AI-900 Exam Prep Hub main page.