Important Microsoft AI-300 Exam Questions

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 data science team trains a classification model that predicts loan approval outcomes.

Before registering the model, the team must ensure the following:

Predictions must not disproportionately impact protected groups.

Prediction errors can be evaluated across different data segments.

You need to assess whether the model meets Responsible AI expectations.

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

You create a binary classification model. You use the Fairlearn package to assess model fairness.

You must eliminate the need to retrain the model.

You need to implement the Fairlearn package.

Which algorithm should you use?

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?

You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.'s issues, constraints, and technical requirements.

What should you implement?