AI-300 Latest Study Questions, AI-300 Exam Simulator Fee

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Microsoft AI-300 Exam Syllabus Topics:

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

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                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q18-Q23):

                                  NEW QUESTION # 18
                                  You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.
                                  You start by writing the following code segment:

                                  For each of the following statements, select Yes If the statement is true. Otherwise, select No.

                                  Answer:

                                  Explanation:

                                  Explanation:


                                  NEW QUESTION # 19
                                  A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
                                  The team needs to introduce a new version of a model to production without disrupting existing users.
                                  The team must validate the new version before full rollout.
                                  You need to reduce risk during deployment.
                                  What should you do?

                                  Answer: D

                                  Explanation:
                                  To introduce a new model version in Azure Machine Learning without service interruption, you should use Blue/Green Deployment with Traffic Splitting.
                                  This strategy allows you to run two versions of a model simultaneously under a single Online Endpoint, gradually shifting users to the new version once it is validated.
                                  Key Benefits
                                  Zero Downtime: The endpoint URL stays the same; only the backend routing changes.
                                  Easy Rollback: If the new model fails, you can instantly flip traffic back to 100% on the old version.
                                  Risk Mitigation: Only a small subset of users is exposed to the unproven model initially.
                                  Implementation Steps
                                  1. Create the "Green" Deployment
                                  Deploy the new model version as a second deployment under the existing online endpoint.
                                  Initially, set its traffic allocation to 0%.
                                  2. Canary Testing (Initial Split)
                                  Shift a small percentage of traffic (e.g., 10%) to the new deployment. Monitor performance metrics, error rates, and model accuracy in a real-world environment.
                                  3. Validation & Monitoring
                                  Use Azure Monitor and Application Insights to compare the two deployments. Check for:
                                  Latency: Is the new model slower?
                                  HTTP Status Codes: Are there 4xx or 5xx errors?
                                  Model Drift: Is the prediction quality as expected?
                                  4. Full Rollout
                                  If the new version is stable, increase the traffic split (e.g., 50/50) until the new model handles
                                  100% of the traffic.
                                  5. Cleanup
                                  Once the "Green" deployment is confirmed as the new production standard, you can delete the old ("Blue") deployment to save costs.
                                  Reference:
                                  https://learn.microsoft.com/en-us/azure/well-architected/ai/operations


                                  NEW QUESTION # 20
                                  You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
                                  There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
                                  You need to exclude the 20 files. What should you do?

                                  Answer: B


                                  NEW QUESTION # 21
                                  You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
                                  You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs in the workspace.
                                  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

                                  Answer:

                                  Explanation:

                                  Explanation:


                                  NEW QUESTION # 22
                                  You manage an Azure Machine learning workspace named workspace1.
                                  You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
                                  You need to add the instantiated compute instance to workspace 1.
                                  What should you use?

                                  Answer: B


                                  NEW QUESTION # 23
                                  ......

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