Free Practice Questions for Microsoft AI-300 Exam

You are fine-tuning a base language model to analyze customer feedback.

You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.

You need to configure and run fine-tuning.

What should you do first?

You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.

You need to select an evaluation metric.

Which built-in evaluator 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 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 train.py --training_data training_data

Does the solution meet the goal?

A data science team completes multiple training runs within an experiment by using MLflow.

The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.

The model must be versioned centrally for reuse across environments.

You need to version the trained model.

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

A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.

A deployed online endpoint shows inconsistent response times during periods of high traffic.

You need to identify potential performance degradation.

Which three metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose three