Microsoft : AI-300 Questions & Answers

Last Updated: Sep 10, 2026

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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Implement secure and scalable AI systems- Scalability and performance optimization
  • 1. Cost optimization strategies
    • 2. Autoscaling AI workloads
      - Security and governance
      • 1. Identity and access management for AI services
        • 2. Data privacy and compliance considerations
          Design and implement generative AI solutions- Large language model integration
          • 1. Prompt engineering and prompt flow design
            • 2. Use Azure OpenAI Service capabilities
              - RAG (Retrieval Augmented Generation) solutions
              • 1. Knowledge grounding and retrieval design
                • 2. Vector search integration
                  Plan and design AI solutions using Azure AI services- Responsible AI design
                  • 1. Responsible AI mitigation strategies
                    • 2. Fairness, transparency, and accountability considerations
                      - Requirements gathering and solution architecture
                      • 1. Select appropriate Azure AI services
                        • 2. Identify business requirements for AI solutions
                          Operationalizing machine learning solutions- Deployment and monitoring
                          • 1. Deploy models to endpoints
                            • 2. Monitor performance and drift
                              - ML lifecycle management
                              • 1. Model versioning and registry usage
                                • 2. Model training and evaluation in Azure Machine Learning

                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                  Question #1

                                  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.

                                  Answer:

                                  Question #2

                                  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?

                                  • A. Azure Machine Learning job configuration
                                  • B. Azure Machine Learning job input/output bindings
                                  • C. Azure Monitor alert
                                  • D. Azure Machine Learning environment
                                  Answer: D

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                                  Question #3

                                  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.

                                  Answer:

                                  Question #4

                                  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?

                                  • A. Increase max_tokens and temperature to reduce the chance of early termination.
                                  • B. Replace the deployed model with a smaller model to reduce variability across responses.
                                  • C. Enable tracing and logging so that each workflow can be inspected.
                                  • D. Run a pre-release evaluation workflow to score groundedness and relevance on a test dataset.
                                  Answer: C

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                                  Question #5

                                  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?

                                  • A. Create a tuning job that runs multiple trials with different parameter values.
                                  • B. Export training metrics and compare results outside Azure Machine Learning.
                                  • C. Adjust hyperparameters after model deployment.
                                  • D. Run a single training job with fixed hyperparameters.
                                  Answer: A

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