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Free AI-300 Practice Test (2026) - Microsoft AI-300 MLOps Questions

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Question 1 of 10

Objective 2.3 Implement machine learning model lifecycle and operations

After a model replacement, an operations team must retire an unused managed online endpoint and ensure every deployment hosted under it is removed. What action is being performed?

Concept tested: Implement machine learning model lifecycle and operations (2.3)

Source:Deploy Machine Learning Models to Online Endpoints - Azure Machine Learning

Question 2 of 10

Objective 1.1 Design and implement an MLOps infrastructure

An Azure Machine Learning workspace has been created for a team. The administrator must control which users can create resources, run jobs, and manage assets. What should be configured?

Concept tested: Design and implement an MLOps infrastructure (1.1)

Question 3 of 10

Objective 2.1 Implement machine learning model lifecycle and operations

A data science team wants to work together in Azure Machine Learning by sharing notebooks, compute resources, data, and environments. Which capability supports this collaboration?

Concept tested: Implement machine learning model lifecycle and operations (2.1)

Question 4 of 10

Objective 1.3 Design and implement an MLOps infrastructure

A release engineer wants a GitHub tag to start the Azure Machine Learning build and deployment workflow for a model package. Which tool is identified for this repository-based automation?

Concept tested: Design and implement an MLOps infrastructure (1.3)

Question 5 of 10

Objective 2.2 Implement machine learning model lifecycle and operations

Which Azure Machine Learning feature enables the tracking of lineage between jobs and assets such as containers, data, and compute resources?

Concept tested: Implement machine learning model lifecycle and operations (2.2)

Question 6 of 10

Objective 1.2 Design and implement an MLOps infrastructure

A team needs to share Azure Machine Learning resources across multiple engineers, including registered data, environments, components, models, and endpoints. What resource should they use as the shared project container?

Concept tested: Design and implement an MLOps infrastructure (1.2)

Question 7 of 10

Objective 2.4 Implement machine learning model lifecycle and operations

A fraud model is in production, and the input distribution has started to shift after a product launch. The team wants model refresh work to begin when drift analysis crosses an approved threshold. Which action helps maintain model performance?

Concept tested: Implement machine learning model lifecycle and operations (2.4)

Question 8 of 10

Objective 1.3 Design and implement an MLOps infrastructure

A platform team wants notebooks and training code developed for Azure Machine Learning to be versioned through Git repositories instead of being managed only inside the workspace UI. Which feature supports that workflow?

Concept tested: Design and implement an MLOps infrastructure (1.3)

Question 9 of 10

Objective 2.1 Implement machine learning model lifecycle and operations

A machine learning team wants flexibility to use tools that fit its existing workflow while working with Azure Machine Learning. Which capability supports that approach?

Concept tested: Implement machine learning model lifecycle and operations (2.1)

Question 10 of 10

Objective 1.3 Design and implement an MLOps infrastructure

A team wants Azure Machine Learning pipeline activity to trigger or connect with event-driven automation. Which integration supports that pattern?

Concept tested: Design and implement an MLOps infrastructure (1.3)

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Question 1 After a model replacement, an operations team must retire an unused managed online endpoint and ensure every deployment hosted under it is removed. What action is being performed?

Answer choices

A. To monitor real-time inference performance
B. To update an existing endpoint configuration
C. To remove an endpoint and all its underlying deployments
D. To create a new managed online endpoint

Correct answer

To remove an endpoint and all its underlying deployments

Removing the endpoint and its underlying deployments retires the serving resource and cleans up the deployments beneath it. Monitoring, updating, or creating an endpoint keeps a serving resource active instead of retiring it.

Wrong-answer review

A. To monitor real-time inference performance: Monitoring real-time inference performance observes metrics for an active service rather than deleting resources.
B. To update an existing endpoint configuration: Updating an existing endpoint configuration changes settings on a deployed service rather than retiring it.
D. To create a new managed online endpoint: Creating a new managed online endpoint provisions a new serving resource rather than cleaning up an old one.

Objective/domain: Implement machine learning model lifecycle and operations (2.3)

Question 2 An Azure Machine Learning workspace has been created for a team. The administrator must control which users can create resources, run jobs, and manage assets. What should be configured?

A. Configure storage accounts
B. Define role-based access control (RBAC)
C. Create a compute target
D. Set up datastores

Define role-based access control (RBAC)

Objective/domain: Design and implement an MLOps infrastructure (1.1)

Source: Tutorial: Create workspace resources - Azure Machine Learning

Question 3 A data science team wants to work together in Azure Machine Learning by sharing notebooks, compute resources, data, and environments. Which capability supports this collaboration?

A. Automated hyperparameter tuning
B. Shared notebooks collaboration
C. Cross-compatible platform tools
D. Model deployment services

Shared notebooks collaboration

Objective/domain: Implement machine learning model lifecycle and operations (2.1)

Source: What is Azure Machine Learning? - Azure Machine Learning

Question 4 A release engineer wants a GitHub tag to start the Azure Machine Learning build and deployment workflow for a model package. Which tool is identified for this repository-based automation?

A. Azure DevOps
B. Jenkins
C. Apache Airflow
D. GitHub Actions

GitHub Actions

Objective/domain: Design and implement an MLOps infrastructure (1.3)

Question 5 Which Azure Machine Learning feature enables the tracking of lineage between jobs and assets such as containers, data, and compute resources?

A. MLflow integration
B. Azure Event Grid integration
C. Job artifacts
D. Git integration

Job artifacts

Objective/domain: Implement machine learning model lifecycle and operations (2.2)

Question 6 A team needs to share Azure Machine Learning resources across multiple engineers, including registered data, environments, components, models, and endpoints. What resource should they use as the shared project container?

A. Data assets
B. Environments
C. Workspace
D. Components

Workspace

Objective/domain: Design and implement an MLOps infrastructure (1.2)

Question 7 A fraud model is in production, and the input distribution has started to shift after a product launch. The team wants model refresh work to begin when drift analysis crosses an approved threshold. Which action helps maintain model performance?

A. Disable all alerts and ignore performance metrics
B. Configure retraining triggers based on data drift analysis
C. Use 'az ml online-endpoint delete' command for monitoring
D. Deploy new versions without any monitoring setup

Configure retraining triggers based on data drift analysis

Objective/domain: Implement machine learning model lifecycle and operations (2.4)

Question 8 A platform team wants notebooks and training code developed for Azure Machine Learning to be versioned through Git repositories instead of being managed only inside the workspace UI. Which feature supports that workflow?

A. Apache Airflow package
B. MLflow integration
C. Git integration
D. Azure Event Grid integration

Git integration

Question 9 A machine learning team wants flexibility to use tools that fit its existing workflow while working with Azure Machine Learning. Which capability supports that approach?

A. Cross-compatible platform tools that meet your needs
B. Deploy ML models quickly and easily at scale
C. Collaborate via shared notebooks
D. Develop models for fairness and explainability

Cross-compatible platform tools that meet your needs

Question 10 A team wants Azure Machine Learning pipeline activity to trigger or connect with event-driven automation. Which integration supports that pattern?

A. MLflow integration
B. Git integration
C. Apache Airflow
D. Azure Event Grid integration

Azure Event Grid integration

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Practice with explanations that teach.

After a model replacement, an operations team must retire an unused managed online endpoint and ensure every deployment hosted under it is removed. What action is being performed?

An Azure Machine Learning workspace has been created for a team. The administrator must control which users can create resources, run jobs, and manage assets. What should be configured?

A data science team wants to work together in Azure Machine Learning by sharing notebooks, compute resources, data, and environments. Which capability supports this collaboration?

A release engineer wants a GitHub tag to start the Azure Machine Learning build and deployment workflow for a model package. Which tool is identified for this repository-based automation?

Which Azure Machine Learning feature enables the tracking of lineage between jobs and assets such as containers, data, and compute resources?

A team needs to share Azure Machine Learning resources across multiple engineers, including registered data, environments, components, models, and endpoints. What resource should they use as the shared project container?

A fraud model is in production, and the input distribution has started to shift after a product launch. The team wants model refresh work to begin when drift analysis crosses an approved threshold. Which action helps maintain model performance?

A platform team wants notebooks and training code developed for Azure Machine Learning to be versioned through Git repositories instead of being managed only inside the workspace UI. Which feature supports that workflow?

A machine learning team wants flexibility to use tools that fit its existing workflow while working with Azure Machine Learning. Which capability supports that approach?

A team wants Azure Machine Learning pipeline activity to trigger or connect with event-driven automation. Which integration supports that pattern?

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