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Microsoft AI-300 Exam Syllabus Topics:

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. Feature count
B. Requests per minute
C. Dataset size
D. Connections active
E. Request latency

Question 2

Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
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. Disable all inbound network access.
B. Configure a managed virtual network for the Microsoft Foundry resource.
C. Deploy the Microsoft Foundry resource in a separate Azure subscription.
D. Disable public network access to the Microsoft Foundry resource.
E. Use API key authentication for all model endpoints.

Question 4

Drag and Drop Question
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute1 by using a terminal window from workspace1.
You create a file named "requirements.txt" containing Python dependencies to include Jupyter.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Question 5

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 a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Does the solution meet the goal?

A. No
B. Yes

Solutions: