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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?

A. GitHub Actions workflow that executes the runs
B. Content filter configured in warning mode
C. Blocklist applied to the model endpoint
D. Retrieval chunking strategy

Question 2

You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?

A. !pip
B. !conda
C. %pip
D. %load

Question 3

Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
- name="mlcluster1"
- size="STANDARD_DS3_v2"
- min_instances=1
- max_instances=4
- tier="dedicated"
The cost of the compute resources must be minimized when a workload is active or idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 4

Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

A. Model registry
B. Git repositories
C. Data lake
D. Blob storage containers

Question 5

A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?

A. Enable retry policy on pipeline step
B. Run pipeline manually
C. Increase compute size
D. Remove failing step

Solutions: