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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

Hotspot Question
You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records.
You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:
- number of non-empty values
- average value
- standard deviation
- minimum and maximum values
- 25th, 50th, and 75th percentiles
You need to complete the Python code that will generate the summary statistics.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 2

A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A. Two independent pipelines with shared scripts
B. Azure Function to retrain model manually
C. Notebook-based retraining process
D. One pipeline triggered by schedule and data drift alerts

Question 3

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: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?

A. Yes
B. No

Question 4

An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?

A. Implement an evaluation step by using GitHub Actions.
B. Review evaluation results manually after deployment.
C. Monitor latency metrics during model inference.
D. Enable application tracing to collect runtime telemetry.

Question 5

Hotspot Question
You manage an Azure OpenAI deployment of the gpt-4o base model.
You plan to fine-tune the deployed model.
You need to prepare a file that contains training data.
Which keys should you include in each line of the training data file? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: