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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
- 1. Implement logging and alerting
- 2. Track usage, performance, and errors
- Evaluate and test generative AI applications
- 1. Test for safety, accuracy, and relevance
- 2. Define evaluation metrics and criteria
|
| 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. Manage compute targets, datastores, and environments
- 2. Configure workspace settings and security
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
- 1. Choose appropriate models and parameters
- 2. Tune prompts and generation settings
- Improve efficiency and cost-effectiveness
- 1. Manage resource utilization
- 2. Optimize inference and deployment
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
- 1. Configure deployment options and scaling
- 2. Deploy to real-time and batch endpoints
- Monitor and maintain models in production
- 1. Monitor data and model drift
- 2. Implement retraining and update workflows
- Register, version, and package models
- 1. Create reusable model packages
- 2. Manage model registry
- Orchestrate model training and experimentation
- 1. Create and manage pipelines
- 2. Track experiments and metrics
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
- 1. Integrate with Azure services and tools
- 2. Design scalable and secure architecture
- Set up Microsoft Foundry environment
- 1. Manage compute and deployment resources
- 2. Configure projects, connections, and security
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. 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.

2. Hotspot Question
A team is preparing a generative AI application for production deployment. The application generates structured responses that must be evaluated for quality before each release.
The organization requires repeatable evaluation results that can be compared across builds and environments.
You need to configure evaluation inputs so quality metrics can be reliably calculated across test runs.
How should you prepare the evaluation inputs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. Hotspot Question
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. Drag and Drop Question
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
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.

5. Hotspot Question
You monitor an Azure Machine Learning classification training experiment named train_classification on Azure Notebooks.
You must store a table named table as an artifact in Azure Machine Learning Studio during model training.
You need to collect and list the metrics by using MLflow.
How should you complete the code segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Solutions:
Question # 1 Answer: Only visible for members | 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 |