Exam AI-103 Topic 1 Question 121 Discussion
Actual exam question for Microsoft's AI-103 exam
Question #: 121
Topic #: 1
Question #: 121
Topic #: 1
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
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You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?
Suggested Answer: B Vote an answer
The solution does not meet the goal. A completeness evaluation flow is useful for detecting incomplete responses, but detection and blocking do not improve the response itself. Microsoft Foundry RAG evaluators define Response Completeness as a metric that measures whether a response covers all critical information from the expected response or ground truth. It is a system evaluation signal used to assess response quality and produce pass/fail or scored results.
In this scenario, the issue is that the agent omits required regulatory clauses even though the clauses are present in retrieved content. Blocking low-scoring responses would prevent incomplete answers from being returned, but it would not revise the summary, add the missing clauses, or improve the generation process.
The appropriate improvement is to add a response-generation control such as a reflection or verification pass that checks the draft summary against the retrieved policy content and regenerates or amends the answer before returning it. Evaluation can support the quality gate, but by itself it is an assessment mechanism, not a completeness-enhancement mechanism. Reference topics: Microsoft Foundry RAG evaluators, response completeness, grounded generation, reflection, and response quality optimization.
In this scenario, the issue is that the agent omits required regulatory clauses even though the clauses are present in retrieved content. Blocking low-scoring responses would prevent incomplete answers from being returned, but it would not revise the summary, add the missing clauses, or improve the generation process.
The appropriate improvement is to add a response-generation control such as a reflection or verification pass that checks the draft summary against the retrieved policy content and regenerates or amends the answer before returning it. Evaluation can support the quality gate, but by itself it is an assessment mechanism, not a completeness-enhancement mechanism. Reference topics: Microsoft Foundry RAG evaluators, response completeness, grounded generation, reflection, and response quality optimization.
by Berton at Aug 22, 2026, 11:24 AM
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