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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Topic 2: Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 3: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Topic 4: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets |
| Topic 5: Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
2. 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: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?
A) Yes
B) No
3. Drag and Drop Question
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible AI (RAI) dashboard for model1.
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. A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?
A) Token limits
B) Chunk size and overlap
C) Temperature parameter
D) Embedding strategy
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 offers enterprise governance controls when workloads process regulated business data
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) The largest available model to simplify operational management
E) A model that supports multiple input types when workloads require combined text and image analysis
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: B | Question # 3 Answer: Only visible for members | Question # 4 Answer: B | Question # 5 Answer: A,B,D |
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