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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement an MLOps infrastructure | - 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 - Manage environments, data stores, and model registries |
| 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 generative AI quality assurance and observability | - 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 - Monitor latency, token usage, cost, and error rates |
| Topic 4: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 5: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - 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 |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. Drag and Drop Question
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four 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.
2. You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?
A) Use same compute for training and inference
B) Increase endpoint compute size
C) Use batch endpoints
D) Define and reuse environment configurations
3. 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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?
A) Yes
B) No
4. Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
5. You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
A) gpt-4
B) gpt-4o
C) gpt-35-turbo
D) davinci-002
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: Only visible for members | Question # 5 Answer: B |
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