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

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Drag and Drop Question
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four 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 team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?

A) Delete the endpoint and immediately redeploy it.
B) Roll back traffic to the previous deployment.
C) Increase the compute size.
D) Change the authentication type to Azure Machine Learning token-based authentication.

3. You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?

A) Enable A/B testing and capture live user traffic for data generation.
B) Run a simulator to produce telemetry logs and trace data from user interactions.
C) Export model evaluation logs and use them directly as training data.
D) Use a simulator to generate prompt-response interaction data that matches the target task.

4. 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.

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 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: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Does the solution meet the goal?

A) No
B) Yes

Solutions: