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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
- 1. Test for safety, accuracy, and relevance
- 2. Define evaluation metrics and criteria
- Monitor generative AI systems
- 1. Track usage, performance, and errors
- 2. Implement logging and alerting
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
- 1. Create and manage pipelines
- 2. Track experiments and metrics
- Deploy models to production
- 1. Deploy to real-time and batch endpoints
- 2. Configure deployment options and scaling
- Monitor and maintain models in production
- 1. Implement retraining and update workflows
- 2. Monitor data and model drift
- Register, version, and package models
- 1. Create reusable model packages
- 2. Manage model registry
|
| Topic 3: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
- 1. Tune prompts and generation settings
- 2. Choose appropriate models and parameters
- Improve efficiency and cost-effectiveness
- 1. Optimize inference and deployment
- 2. Manage resource utilization
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
- 1. Use Bicep or Azure CLI to deploy resources
- 2. Automate infrastructure provisioning
- Create and manage Machine Learning workspace resources and assets
- 1. Configure workspace settings and security
- 2. Manage compute targets, datastores, and environments
|
| Topic 5: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
- 1. Design scalable and secure architecture
- 2. Integrate with Azure services and tools
- Set up Microsoft Foundry environment
- 1. Configure projects, connections, and security
- 2. Manage compute and deployment resources
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question 1
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.
A. Dataset size
B. Connections active
C. Request latency
D. Requests per minute
E. Feature count
Question 2
Drag and Drop Question
You manage an Microsoft Foundry project.
You deploy a large language model from the model catalog.
You need to manually evaluate the model, collect the statistics, and be able to review the results later.
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.

Question 3
Drag and Drop Question
A team is developing a Retrieval-Augmented Generation (RAG) system.
The team requires improvements to the system's retrieval quality to ensure accurate, grounded responses.
You need to assess RAG performance before you can suggest an improvement strategy.
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.

Question 4
Hotspot Question
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Question 5
Hotspot Question
You create an Azure Machine Learning workspace.
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script, environment, and compute information.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions:
Question 1 Answer: B,C,D | Question 2 Answer: Only visible for members | Question 3 Answer: Only visible for members | Question 4 Answer: Only visible for members | Question 5 Answer: Only visible for members |