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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - Large language model integration
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:

You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
Correct Answer: A 🗳️
Explanation: Only visible for ExamsReviews members. You can sign-up / login (it's free).
Drag and Drop Question
A team is developing a Retrieval-Augmented Generation (RAG) system.
The team requires improvements to the system's retrieval quality to ensure accurate, grounded responses.
You need to assess RAG performance before you can suggest an improvement strategy.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:

Explanation:
To properly assess your Azure Retrieval-Augmented Generation (RAG) system's performance before implementing an improvement strategy, you should include the following four steps: Run RAG evaluators, Collect retrieval logs, Modify model temperature, and Regenerate the prompt template.
Step 1: Run RAG evaluators
Run RAG evaluators is the primary method to objectively measure system performance.
Evaluators calculate data-driven metrics like groundedness, relevance, and retrieval precision using tools like Azure AI Studio Evaluators.
Step 2: Collect retrieval logs
Collect retrieval logs provides the raw operational data needed for assessment. Analyzing these logs helps you identify exactly which documents were retrieved, their relevance scores, and where the retrieval pipeline failed to fetch the correct context.
Step 3: Modify model temperature
Modify model temperature: Adjusting the temperature during assessment helps isolate whether poor responses are caused by bad retrieval or by the LLM being too creative (high temperature) or too rigid (low temperature). Testing variations helps establish a performance baseline.
Step 4: Regenerate the prompt template
Regenerate the prompt template: Evaluating how different prompt variations alter the output allows you to assess if the current template is effectively forcing the model to rely only on the retrieved context, which is critical for identifying grounding issues.
Incorrect:
Adjust the chunking strategy
This is an improvement action, not an assessment step. You would perform this optimization strategy after your assessment reveals that information is being cut off or poorly contextualized.
Re-index documents: This is a heavy remediation step. Re-indexing is a time- and resource- consuming strategy used to fix issues once the assessment phase has already proven that the current index or embedding model is faulty.
Reference:
https://flytoleisure.medium.com/guideline-for-building-a-practical-and-effective-rag-retrieval-augmented-generation-application-f6cf50676e37
Hotspot Question
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:

You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Correct Answer: C 🗳️
Explanation: Only visible for ExamsReviews members. You can sign-up / login (it's free).
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