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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 2: Implement secure and scalable AI systems | - Security and governance
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 4: Operationalizing machine learning solutions | - Deployment and monitoring
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
2. Drag and Drop Question
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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.
3. Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
4. 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) Share a single API key across all teams.
D) Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.
5. Hotspot Question
You manage a Microsoft Foundry project.
You plan to build a RAG solution.
The solution must include two models:
- One for text output, named Model1. This model must resemble human
language and read naturally.
- One for creating embeddings, named Model2. This model must maximize
the retrieval of relevant results (high recall) while minimizing
irrelevant or incorrect matches (high precision).
You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.
Which benchmarking metric should you select for each model? To answer, select the appropriate options 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: A,B | Question # 5 Answer: Only visible for members |






