AI-300 Free Practice Questions — Page 5

Question 41

HOTSPOT - A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry. The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments. You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment. What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 42

DRAG DROP - An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve. The application intermittently times out during production use, which requires ongoing visibility into runtime behavior. The team must also validate model quality and safety before releasing new updates to avoid introducing regressions. You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation. Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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.

Question 43

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?

Question 44

HOTSPOT - You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry. The fine-tuning job uses preference comparison data. You review the following dataset excerpt. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Question 45

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?

Question 46

You manage a Microsoft Foundry project. You build a multi-turn chatbot application. 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?

Question 47

A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source. You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs. Which two actions should you perform? Each correct answer presents part of the solution. Choose two. NOTE: Each correct selection is worth one point.

Question 48

A team develops and manages a conversational assistant by using Microsoft Foundry. The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users. You need to evaluate the model output for hateful responses as part of a repeatable validation process. Which evaluator should you configure first?

Question 49

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?

Question 50

A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience. When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct. You need to evaluate the quality of the language from the generated responses. Which evaluator should you use?