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Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Exam

Attempt the Machine Learning Operations (MLOps) Engineer Associate practice test and solve real exam-like AI-300 questions to prepare efficiently and increase your chances of success. Our Microsoft AI-300 practice questions match the actual Operationalizing Machine Learning and Generative AI Solutions exam format, helping you enhance confidence and improve performance. With our AI-300 practice exam software, you can analyze your performance, identify weak areas, and work on them effectively to boost your final Machine Learning Operations (MLOps) Engineer Associate exam score.

Vendor: Microsoft
Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Registration Code: AI-300
Related Certification: Microsoft Machine Learning Operations (MLOps) Engineer Associate Certification
Exam Audience: AI Engineer,

Total Questions

60

Last Updated

23-08-2026

Exam Duration

120 MINUTES

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Question: 1

A company's platform engineers manage the resource settings and governance of Microsoft Foundry.

Developers must be able to create and update project assets but must not be able to change resource-level configurations.

You need to enforce least privilege access for the engineers and developers.

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.

Question: 2

A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.

The team requires a safe way to validate a new model version without disrupting existing users.

You need to recommend a deployment strategy for controlled testing of a new model version.

What should you configure?

Question: 3

A data science team completes multiple training runs within an experiment by using MLflow.

The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.

The model must be versioned centrally for reuse across environments.

You need to version the trained model.

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.

Question: 4

A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.

A deployed online endpoint shows inconsistent response times during periods of high traffic.

You need to identify potential performance degradation.

Which three metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose three

Question: 5

You have a deployment of an Azure OpenAI Service base model.

You plan to fine-tune the model.

You need to prepare a file that contains training data.

Which file format should you use?

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