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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Design and implement an MLOps infrastructure	15–20%	- Create and manage Machine Learning workspace resources and assets 1. Configure workspace settings and security 2. Manage compute targets, datastores, and environments - Implement infrastructure as code for Machine Learning 1. Automate infrastructure provisioning 2. Use Bicep or Azure CLI to deploy resources
Topic 2: 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. Optimize inference and deployment 2. Manage resource utilization
Topic 3: 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. Define evaluation metrics and criteria 2. Test for safety, accuracy, and relevance
Topic 4: 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. Configure projects, connections, and security 2. Manage compute and deployment resources
Topic 5: 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 - Register, version, and package models 1. Manage model registry 2. Create reusable model packages - Monitor and maintain models in production 1. Monitor data and model drift 2. Implement retraining and update workflows - Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. 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.

2. A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Assign the Azure AI Developer role to the developers.
B) Assign a resource-level Azure AI Administrator role to the platform engineers.
C) Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.
D) Share a single API key across all teams.

3. Drag and Drop Question
A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.
The team must:
- Track prompt changes with a clear history for audit and rollback.
- Compare prompt variants in parallel without affecting the prompt used in the production environment.
You need to select the appropriate source control approach for each requirement.
What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control once, more than once, or not at all.
You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

4. Hotspot Question
You review the following Azure CLI command and the relevant Bicep excerpt.

(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

5. A team is validating a generative AI assistant for a company. The assistant generates responses by using internal knowledge sources.
The company requires assurance that responses are accurate, supported by sources, and related to the user prompts before enabling production access.
You need to implement quality metrics that confirm the assistant produces reliable and meaningful responses.
Which two evaluation metrics should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Harmfulness
B) Relevance
C) Groundedness
D) Tone
E) Fairness

Solutions:

Question # 1
Answer: Only visible for members

Question # 2
Answer: A,B

Question # 3
Answer: Only visible for members

Question # 4
Answer: Only visible for members

Question # 5
Answer: B,C