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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Your model requires access to external APIs using sensitive credentials during inference. You must ensure credentials are not exposed in code, logs, or environment variables. What should you implement?

A) Store in config files
B) Hardcode credentials
C) Encrypt credentials locally
D) Use Azure Key Vault with managed identity

2. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 - AzureML kernel.
Does the solution meet the goal?

A) Yes
B) No

3. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) Conditional logic in pipeline
B) GitHub pull request checks
C) Azure Monitor alerts
D) Manual approval gate

4. You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) Change the current kernel.
B) Reset the compute.
C) Change the compute.
D) Navigate to another section of the workspace.
E) stop the current kernel.

5. An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?

A) Review evaluation results manually after deployment.
B) Enable application tracing to collect runtime telemetry.
C) Implement an evaluation step by using GitHub Actions.
D) Monitor latency metrics during model inference.

Solutions: