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

A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.

The team requires a centralized way to track, version, and reuse prompt content across projects.

You need to recommend a solution to track and reuse prompt content.

Which approach should you recommend?

An organization maintains separate Azure Machine Learning workspaces for development and production.

Both environments must use the same validated assets without duplicating them.

Assets must be shared across workspaces while maintaining centralized governance and version control.

You need to enable reuse of assets across workspaces without copying them.

What should you do?

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 --trainingdata ${{inputs.training_data}}

Does the solution meet the goal?

A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.

The team needs to introduce a new version of a model to production without disrupting existing users.

The team must validate the new version before full rollout.

You need to reduce risk during deployment.

What should you do?

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?

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: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.

Does the solution meet the goal?

A company's platform engineers manage the resource settings and governance of Microsoft Foundry.

Developers must be able to create and update project assets but must not be able to change resource-level configurations.

You need to enforce least privilege access for the engineers and developers.

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.