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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Topic 2: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates |
| Topic 3: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 4: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput |
| Topic 5: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. Drag and Drop Question
A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
- Retrieve feature values dynamically at inference time.
- Use the same feature definitions that were used during training.
- Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity 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.
2. DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. 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.
3. An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
A) Publish the asset to an Azure Machine Learning registry.
B) Enable workspace-level Git integration and sync assets between repositories.
C) Create a shared Azure Machine Learning environment that includes the asset.
D) Publish the asset as a pipeline component.
4. A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?
A) Content safety
B) Indirect attacks
C) Protected material
D) Groundedness
5. A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
A) Increase token limits for all requests.
B) Implement an optimized re-ranker.
C) Tune chunk size and overlap to match content structure.
D) Optimize the length of embedding vectors.
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: Only visible for members | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B,C |
