AI-300 Practice Questions — Page 2

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 deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production.

When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems.

You need to configure the monitoring solution to meet the requirements.

Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

You train a model in Azure Machine Learning.

You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.

You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

A team deploys a classification model to production and monitors performance and data changes.

The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:

Stakeholders must be notified of the drops.

Retraining must be initiated when thresholds are exceeded

You need to configure monitoring to meet the requirements.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

A team runs training jobs by using multiple Azure Machine Learning pipelines.

The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.

You need to configure the workspace so that runtime dependencies are consistent and reusable.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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 company plans to deploy a foundation model in Microsoft Foundry.

The mode must support the following workloads:

A customer support workload used across multiple regions

A marketing workload that must remain within a specific region due to data residency requirements

You need to select the deployment type.

Which deployment type should you use for each workload? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

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?

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: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.

Does the solution meet the goal?

A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load.

You need to enable performance observability.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.