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Updated: Jul 29, 2026

No. of Questions: 159 Questions & Answers with Testing Engine

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Microsoft AI-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Operationalizing Machine Learning and Generative AI Solutions
Exam Number:AI-300
Exam Duration:Not officially published / TBD
Passing Score:Not officially published / TBD
Related Certifications:Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure Solutions Architect Expert
Exam Price:Varies by region (typically ~USD 165, subject to Microsoft regional pricing)
Available Languages:English
Exam Format:Multiple choice, Multiple response, Case studies, Scenario-based questions
Real Exam Qty:Not officially published / TBD
Certificate Validity Period:Typically 1 year (renewable, depending on Microsoft certification policy)
Recommended Training:Azure OpenAI and Generative AI learning modules
Microsoft Learn Azure AI Engineer learning paths
Exam Registration:Pearson VUE Microsoft Exams
Microsoft Learn Certification Portal
Sample Questions:Microsoft AI-300 Sample Questions
Exam Way:Online proctored or authorized testing center (Pearson VUE, depending on region)
Pre Condition:No formal prerequisite required, but recommended experience with Azure AI services, machine learning concepts, and basic cloud architecture knowledge.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/

Microsoft AI-300 Exam Syllabus Topics:

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

                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                  1. 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 manage a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
                                  The application retrieves documents from an indexed knowledge base and generates answers for internal users.
                                  Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
                                  You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
                                  Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
                                  Does the solution meet the goal?

                                  A) No
                                  B) Yes


                                  2. Drag and Drop Question
                                  A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
                                  The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
                                  You need to configure and run an AutoML training job.
                                  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.


                                  3. Hotspot Question
                                  A team manages an Azure Machine Learning workspace to train and register machine learning models.
                                  Previous model versions must be retained for audit and rollback purposes but must not be used for new deployments.
                                  You need to manage model versions.
                                  What should you do for each requirement? To answer, select the appropriate options in the answer area.
                                  NOTE: Each correct selection is worth one point.


                                  4. Drag and Drop Question
                                  A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
                                  Provisioning must be triggered by changes in the templates and executed without manual intervention.
                                  You need to automate resource provisioning.
                                  Which action should you take for each requirement? 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.


                                  5. Hotspot Question
                                  You review the following Azure CLI command and the relevant Bicep excerpt.

                                  (Non-relevant sections are omitted.)
                                  You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                                  NOTE: Each correct selection is worth one point.


                                  Solutions:

                                  Question # 1
                                  Answer: A
                                  Question # 2
                                  Answer: Only visible for members
                                  Question # 3
                                  Answer: Only visible for members
                                  Question # 4
                                  Answer: Only visible for members
                                  Question # 5
                                  Answer: Only visible for members

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