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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?

A) Service endpoints
B) Azure Firewall rules
C) Network security groups
D) Private endpoints

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

3. Hotspot Question
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
- Process text inputs up to 200k tokens long.
- Generate concise summaries in seconds.
- Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. 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) The largest available model to simplify operational management
D) A model that offers enterprise governance controls when workloads process regulated business data
E) The smallest available model to minimize the usage cost

5. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?

A) Yes
B) No

Solutions: