Microsoft Operationalizing Machine Learning and Generative AI Solutions - AI-300

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

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.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
Does the solution meet the goal?

A. Yes
B. No

Question 2

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 in the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?

A. Yes
B. No

Question 3

Hotspot Question
A team retrains a machine learning model on a weekly basis by using updated training data.
The team must be able to rerun any previous experiment by using the exact data that was available at the time it was originally run. The solution must preserve historical versions of the data without duplicating training scripts.
You need to manage the data so that experiments can be reproduced reliably.
Which workspace actions should you perform? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 4

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

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. Remove failing step
D. Increase compute size

Solutions: