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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. 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.

2. A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A) Coherence
B) Fluency
C) Grounded ness
D) Textual similarity

3. You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?

A) Define and reuse environment configurations
B) Increase endpoint compute size
C) Use same compute for training and inference
D) Use batch endpoints

4. A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?

A) Protected material
B) Groundedness
C) Indirect attacks
D) Content safety

5. 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) Monitor accuracy metrics
C) Disable monitoring
D) Use data drift detection

Solutions: