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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.

A) Request latency
B) Connections active
C) Dataset size
D) Requests per minute
E) Feature count

2. An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.

A) A model that supports multiple input types when workloads require combined text and image analysis
B) A model that is optimized for conversational reasoning when deploying an interactive assistant
C) A model that offers enterprise governance controls when workloads process regulated business data
D) The largest available model to simplify operational management
E) The smallest available model to minimize the usage cost

3. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) Conditional logic in pipeline
B) Manual approval gate
C) GitHub pull request checks
D) Azure Monitor alerts

4. A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

A) Generate synthetic interaction data.
B) Configure content filters.
C) Enable observability metrics.
D) Apply a blocklist.

5. Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
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.

Solutions: