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Exam AI-300 Topic 1 Question 12 Discussion
Actual exam question for Microsoft's AI-300 exam
Question #: 12
Topic #: 1
Question #: 12
Topic #: 1
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?
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?
Suggested Answer: D Vote an answer
To implement an automated evaluation step in GitHub Actions for Microsoft Foundry AI, you can use the Microsoft Foundry Evaluation GitHub Action (or the Azure AI Evaluation SDK).
This setup allows you to run both built-in metrics (like groundedness or coherence) and custom safety checks, then fail the build if scores fall below your defined thresholds.
Implementation Steps
1. Define Your Evaluators
You need to configure which metrics to use. Microsoft Foundry supports two main types:
Built-in Metrics: Pre-trained models that score quality (coherence, fluency) and safety (hate, violence, self-harm).
Custom Metrics: Python-based evaluators you define to check domain-specific requirements.
2. Configure the GitHub Actions Workflow
Create a .yml file in your .github/workflows directory. This workflow will:
Trigger on a pull request or commit.
Authenticate with Azure/Foundry.
Run Evaluation using the microsoft/ai-agent-evals action.
Enforce Thresholds to block the release if quality is insufficient.
Key Components for "Block Release" Logic
To ensure the release is blocked, your workflow must include a gating step that interprets the evaluation results:
Reference:
https://learn.microsoft.com/en-us/training/modules/automated-evaluation-genaiops
This setup allows you to run both built-in metrics (like groundedness or coherence) and custom safety checks, then fail the build if scores fall below your defined thresholds.
Implementation Steps
1. Define Your Evaluators
You need to configure which metrics to use. Microsoft Foundry supports two main types:
Built-in Metrics: Pre-trained models that score quality (coherence, fluency) and safety (hate, violence, self-harm).
Custom Metrics: Python-based evaluators you define to check domain-specific requirements.
2. Configure the GitHub Actions Workflow
Create a .yml file in your .github/workflows directory. This workflow will:
Trigger on a pull request or commit.
Authenticate with Azure/Foundry.
Run Evaluation using the microsoft/ai-agent-evals action.
Enforce Thresholds to block the release if quality is insufficient.
Key Components for "Block Release" Logic
To ensure the release is blocked, your workflow must include a gating step that interprets the evaluation results:
Reference:
https://learn.microsoft.com/en-us/training/modules/automated-evaluation-genaiops
by Leif at Jun 01, 2026, 08:56 AM
Here are all the actual test exam dumps for IT exams. Most people prepare for the actual exams with our test dumps to pass their exams. So it's critical to choose and actual test pdf to succeed.
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