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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Drag and Drop Question
A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
- Prompt changes are reviewed before being applied to the version in
production.
- Previous prompt versions can be restored if issues occur.
- Prompt updates follow the same governance practices as the
application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

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

3. A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A) One pipeline triggered by schedule and data drift alerts
B) Two independent pipelines with shared scripts
C) Notebook-based retraining process
D) Azure Function to retrain model manually

4. You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?

A) gpt-35-turbo
B) gpt-4
C) gpt-4o
D) davinci-002

5. You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

A) Batch endpoint validation
B) Separate endpoint for testing
C) Manual testing workflow
D) Traffic splitting with monitoring alerts

Solutions: