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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q10-Q15):

NEW QUESTION # 10
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. Review evaluation results manually after deployment.
C. Enable application tracing to collect runtime telemetry.
D. Implement an evaluation step by using GitHub Actions.

Answer: D

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-us/training/modules/automated-evaluation-genaiops

NEW QUESTION # 11
Hotspot Question
A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
- Changes in input data distribution are detected.
- Appropriate actions are triggered when predefined thresholds are
exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 12
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:

You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 13
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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.
The training_data argument specifies the path to the training data in a file named dataset 1. csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
According to Microsoft ' s Azure Machine Learning SDK v2 documentation on command jobs, input arguments in the command string must exactly match the argument names defined in the Python script ' s argparse configuration. The script reads an argument named training_data with an underscore, but the proposed command passes --trainingdata without the underscore. Python ' s argparse treats these as completely different argument names, so the script would receive None for its --training_data argument, causing a runtime error. The placeholder syntax for referencing the input in the command string is correct, which is a point in this solution ' s favor. However, the argument name mismatch alone is sufficient to make this solution fail. The corrected command would use --training_data exactly matching the argument name defined in the script.
Microsoft Learn Reference Topic: Configure command job inputs and outputs - Azure Machine Learning SDK v2 reference

NEW QUESTION # 14
You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?

A. Same dataset name
B. Same compute SKU
C. Same user identity
D. Versioned datasets and environments

Answer: D

Explanation:
Reproducibility across environments depends on consistent versioning of datasets and environments. Differences in data or dependencies can lead to inconsistent results even if compute resources are identical. Proper asset versioning ensures experiments can be reliably reproduced in any workspace or region.

NEW QUESTION # 15
......

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