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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?

A) Blocklist applied to the model endpoint
B) Content filter configured in warning mode
C) Retrieval chunking strategy
D) GitHub Actions workflow that executes the runs

2. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

A) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
B) Enable tracing for all inference calls in the evaluation pipeline.
C) Deploy the base model to an online endpoint before starting fine-tuning.
D) Use prompt flow to generate multiple prompt templates for evaluation.

3. 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) Content safety
B) Indirect attacks
C) Protected material
D) Groundedness

4. An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?

A) Archive the model version.
B) Archive the training dataset for the model version.
C) Unregister the model version.
D) Delete the model version.

5. Drag and Drop Question
An organization is adopting Microsoft Foundry to support multiple generative AI projects across different teams. Microsoft Foundry platform administrators require centralized governance.
Development teams need isolated environments for experimentation and deployment.
Shared policies must be enforced consistently while allowing teams to work independently.
You need to configure the Microsoft Foundry environment to meet the requirements.
Which Microsoft Foundry components should you configure? To answer, move the appropriate configurations to the correct responsibilities. You may use each configuration 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.

Solutions: