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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Hotspot Question
You manage a Microsoft Foundry project.
You plan to build a RAG solution.
The solution must include two models:
- One for text output, named Model1. This model must resemble human
language and read naturally.
- One for creating embeddings, named Model2. This model must maximize
the retrieval of relevant results (high recall) while minimizing
irrelevant or incorrect matches (high precision).
You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.
Which benchmarking metric should you select for each model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. Drag and Drop Question
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities.
You may use each tool 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.

3. Drag and Drop Question
A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
- Prompt changes are reviewed before being applied to the version in
production.
- Previous prompt versions can be restored if issues occur.
- Prompt updates follow the same governance practices as the
application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

4. 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) Textual similarity
C) Coherence
D) Fluency

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

Solutions: