Last Updated: Sep 10, 2026
No. of Questions: 188 Questions & Answers with Testing Engine
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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Design and implement generative AI solutions | - Large language model integration
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
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.

A team runs training and inference jobs in Azure Machine Learning.
The team experiences inconsistent runtime dependencies that cause variation in results.
You need to ensure that all jobs use the same execution dependencies.
Which asset should you define?
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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.

An organization runs a customer-facing generative AI application built by using Microsoft Foundry. The application uses multiple prompts linked to multiple workflows to generate responses in production.
The application occasionally returns incomplete responses. The model call succeeds, but the final message sometimes stops early.
The issue cannot be reproduced reliably in development.
You need to identify where and why response generation is terminating early in production.
Which approach should you use?
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A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
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