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[Hardware] Microsoft AI-300 Practice Test - 100% Exam Passing Guarantee (2026)

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【Hardware】 Microsoft AI-300 Practice Test - 100% Exam Passing Guarantee (2026)

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q36-Q41):NEW QUESTION # 36
Drag and Drop Question
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load.
You need to enable performance observability.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:


NEW QUESTION # 37
Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:


NEW QUESTION # 38
A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?
  • A. Set minimum instance count
  • B. Switch to batch endpoint
  • C. Increase timeout settings
  • D. Reduce logging
Answer: A
Explanation:
Cold starts occur when instances scale down to zero and must restart to handle new requests.
Setting a minimum instance count ensures that at least one instance is always running, reducing latency spikes. Increasing timeouts does not address the root cause of cold start delays.

NEW QUESTION # 39
Drag and Drop Question
A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
- Prompt changes are reviewed before being applied to the version in
production.
- Previous prompt versions can be restored if issues occur.
- Prompt updates follow the same governance practices as the
application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

Answer:
Explanation:


NEW QUESTION # 40
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?
  • A. Monitor latency metrics during model inference.
  • B. Implement an evaluation step by using GitHub Actions.
  • C. Review evaluation results manually after deployment.
  • D. Enable application tracing to collect runtime telemetry.
Answer: B
Explanation:
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-u ... evaluation-genaiops

NEW QUESTION # 41
......
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