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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution.
(Choose two.)
NOTE: Each correct selection is worth one point.

A) Apply prompt transformations to separate system instructions from user input.
B) Fine-tune the model for each conversational variation.
C) Repeat the instructions at the end of the system prompt.
D) Increase the temperature setting to encourage creativity.
E) Use the system prompt to establish the role, tone, and style.

2. Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Truncation selection
B) Median stopping
C) Default
D) Bandit

4. A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?

A) Store models locally
B) Increase logging
C) Use version pinning and approval workflows
D) Rename models

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

Solutions: