Free Microsoft AI-300 Exam Practice 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 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?

An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.

The organization requires models that meet the following requirements:

Model behavior aligns with the task being performed.

Data handling aligns with internal governance policies.

Operational complexity and cost are justified by workload needs.

You need to select the foundation model options that meet the requirements.

Which three models can you select? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. Choose three.

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?

You need to recommend an experiment-tracking strategy that ensures consistent experiment results.

What should you recommend?