HOTSPOT (Drag and Drop is not supported)You have a Microsoft Foundry project that contains an agent.You use a GitHub Actions workflow for CI/CD.You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.How should you configure the workflow? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: An Azure Login action that uses OpenID Connect(OIDC).The most appropriate authentication method for this scenario is Microsoft Entra ID authentication using OpenIDConnect (OIDC).Using OIDC with the azure/login GitHub Action eliminates the need to store long-lived credentials (like passwords or client secrets) inside GitHub secrets, offering a highly secure and passwordless integration.Why OIDC Is the Recommended ChoiceShort-Lived Tokens: GitHub Actions requests a temporary, scoped token from Microsoft Entra ID dynamically for each workflow run.No Secret Rotations: Eliminates administrative overhead and safety risks associated with managing long-livedAzure Service Principal secrets.Granular RBAC: Permissions can be tightly scoped by assigning the Azure AI User role to the specific repository/workflow identity at the Microsoft Foundry resource level.Box 2: FailConfiguring the GitHub Actions workflow to exit with a non-zero status code (fail) when thresholds are not met is the standard mechanism to block pull requests.To enforce this restriction, you must pair the workflow configuration with GitHub repository settings.Example code:- name: Enforce Thresholds run: |# Use node, python, or bash to read JSON and validate python -c "import json, sys with open('results.json') as f:data = json.load(f)if data['groundedness'] < 0.85:print('Error: Groundedness score below 0.85 threshold.')sys.exit(1)
https://oneuptime.com/blog/post/2026-02-02-github-actions-pull-requests/view
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.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 increase the value of the max_tokens parameter.Does this meet the goal?
Answer(s): B
Correct:* You add a reflection pass that regenerates the response if the required clauses are missing.This is Self-Correction Strategy: A reflection pass allows an agent to evaluate its own initial output against specified constraints (e.g., checking for the presence of mandatory regulatory clauses). If the required text is missing, the agent triggers a programmatic self-correction or regeneration loop to include them before final delivery.Incorrect:* You increase the value of the max_tokens parameter.Increasing the max_tokens parameter prevents the response from being cut off mid-sentence due to length constraints. However, it does not force the model's logic to explicitly include missing information that it chose to leave out earlier in the text.* You increase the value of the temperature parameter.Raising the temperature parameter increases randomness and creativity. For rigid compliance tasks like summarizing regulatory documents, higher temperature actually increases the risk of hallucination and omission.* You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.Evaluation Flow Block: Running an evaluation flow to score completeness and blocking bad responses identifies and stops low-quality outputs, but it does not fix or actively improve the response completeness. It simply filters failures out of the system.
https://pub.towardsai.net/reflection-with-llm-how-to-make-ai-review-its-own-work-2db122fca1d8
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.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 add a reflection pass that regenerates the response if the required clauses are missing.Does this meet the goal?
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.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 increase the value of the temperature parameter.Does this meet the goal?
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.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?
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