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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.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?

A. Yes
B. No

Question 2

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. Use API key authentication for all model endpoints.
B. Deploy the Microsoft Foundry resource in a separate Azure subscription.
C. Disable public network access to the Microsoft Foundry resource.
D. Disable all inbound network access.
E. Configure a managed virtual network for the Microsoft Foundry resource.

Question 3

You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

A. Deploy the model to a managed online endpoint.
B. Convert the model from the MLflow format to a custom format.
C. Create the model explanations.
D. Register the model with the workspace.

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

Question 5

A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A. Set minimum instance count
B. Increase timeout settings
C. Reduce logging
D. Switch to batch endpoint

Solutions: