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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?

A) Use data drift detection
B) Monitor accuracy metrics
C) Disable monitoring
D) Perform manual evaluation only

2. Drag and Drop Question
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities.
You may use each tool once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

3. You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?

A) build
B) image
C) properties
D) conda_file

4. Drag and Drop Question
You manage an Microsoft Foundry project.
You deploy a large language model from the model catalog.
You need to manually evaluate the model, collect the statistics, and be able to review the results later.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

5. You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?

A) Local endpoint
B) Batch endpoint
C) Notebook execution
D) Managed online endpoint

Solutions: