Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Certified Exam Dumps

AI-300 Exam Dumps

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Certification Provider: Microsoft
Exam Code / Number: AI-300
Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Exam Questions: 159
Last Updated: Jul 14, 2026
Corresponding Certification: Microsoft Certified

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Optimize generative AI systems and model performance10–15%- Iterate on generative AI pipeline performance
  • 1. Adjust embedding and vector search configurations
    • 2. Refine model deployment architectures based on observability data
      - Optimize compute, cost, and inference latency
      • 1. Implement caching and resource scaling strategies
        • 2. Select appropriate compute tiers for foundation model workloads
          Topic 2: Implement machine learning model lifecycle and operations25–30%- Orchestrate and automate model training
          • 1. Build training pipelines, run distributed deep learning workloads
            • 2. Track experiments with MLflow, automate hyperparameter tuning
              - Deploy machine learning models to production
              • 1. Configure secure model endpoints and traffic routing
                • 2. Select appropriate deployment targets for batch and real-time inference
                  - Manage model registration, versioning, and evaluation
                  • 1. Evaluate models following responsible AI principles
                    • 2. Register MLflow models, package feature retrieval specifications
                      Topic 3: Deploy and operationalize generative AI solutions25–30%- Design generative AI workloads with Microsoft AI Foundry
                      • 1. Integrate Azure OpenAI Service and foundation models
                        • 2. Implement Retrieval-Augmented Generation (RAG) pipelines
                          - Optimize prompts and fine-tune large language models
                          • 1. Configure low-code and custom fine-tuning workflows
                            • 2. Apply prompt engineering and prompt versioning
                              - Secure and govern generative AI applications
                              • 1. Enforce data privacy and content safety controls
                                • 2. Implement access control for generative AI endpoints
                                  Topic 4: Implement generative AI quality assurance and observability10–15%- Test and validate generative AI outputs
                                  • 1. Automate evaluation of factual accuracy and safety compliance
                                    • 2. Create test datasets for continuous generative AI validation
                                      - Monitor production generative AI systems
                                      • 1. Configure logging, metrics, and alerting for AI workloads
                                        • 2. Detect model drift, response quality degradation, and performance anomalies
                                          Topic 5: Design and implement an MLOps infrastructure15–20%- Create and manage resources in a Machine Learning workspace
                                          • 1. Configure identity, access management, and network restrictions for workspaces
                                            • 2. Create and manage workspaces, datastores, and compute targets
                                              - Create and manage assets in a Machine Learning workspace
                                              • 1. Share cross-workspace assets via registries
                                                • 2. Manage data assets, environments, and reusable components
                                                  - Implement infrastructure as code for Machine Learning
                                                  • 1. Deploy resources with Bicep and Azure CLI
                                                    • 2. Integrate GitHub, GitHub Actions for automated provisioning and source control


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