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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - 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 - Conduct red teaming, adversarial testing, and content filtering |
| Topic 2: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 3: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production |
| Topic 4: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 5: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
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 work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
A) Yes
B) No
2. An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
A) Service endpoints
B) Private endpoints
C) Network security groups
D) Azure Firewall rules
3. Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
Which three 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.
4. 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 supports multiple input types when workloads require combined text and image analysis
B) The smallest available model to minimize the usage cost
C) A model that is optimized for conversational reasoning when deploying an interactive assistant
D) A model that offers enterprise governance controls when workloads process regulated business data
E) The largest available model to simplify operational management
5. You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?
A) Dataset versioning only
B) Logging metrics only
C) Git only
D) Environment + dataset + code versioning
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: Only visible for members | Question # 4 Answer: B,D,E | Question # 5 Answer: D |
