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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Topic 2: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health |
| Topic 3: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 4: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Topic 5: Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. 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) Create a shared Azure Machine Learning environment that includes the asset.
B) Publish the asset as a pipeline component.
C) Enable workspace-level Git integration and sync assets between repositories.
D) Publish the asset to an Azure Machine Learning registry.
2. Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
3. A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
A) Roll back traffic to the previous deployment.
B) Increase the compute size.
C) Delete the endpoint and immediately redeploy it.
D) Change the authentication type to Azure Machine Learning token-based authentication.
4. Drag and Drop Question
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least-privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism 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. An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.
A) A model that is optimized for conversational reasoning when deploying an interactive assistant
B) The smallest available model to minimize the usage cost
C) The largest available model to simplify operational management
D) A model that offers enterprise governance controls when workloads process regulated business data
E) A model that supports multiple input types when workloads require combined text and image analysis
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: Only visible for members | Question # 3 Answer: A | Question # 4 Answer: Only visible for members | Question # 5 Answer: B,C,D |
