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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q61-Q66):

NEW QUESTION # 61
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 # 62
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.

Answer: A,B,E

Explanation:
To locate potential performance degradation in an Azure Machine Learning online endpoint during high traffic, you should monitor these three metrics:
Requests per minute: This metric tracks the volume of incoming traffic and helps identify if spikes in load correlate with slower response times.
Connections active: This monitors the total number of concurrent TCP connections from clients, which can indicate if the endpoint is reaching its capacity limits during peak periods.
Request latency: This directly measures the time taken to respond to requests, allowing you to observe exactly when and by how much performance is degrading.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-deploy-a-machine-learning-model-as-a-real- time-endpoint-in-azure-machine-learning/view


NEW QUESTION # 63
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?

Answer: D

Explanation:
You should use the Hate and Unfairness Evaluator (a specific type of Content Safety Evaluator) in Microsoft Azure AI Foundry. This tool is designed to identify and measure the severity of hateful content toward protected groups, ensuring your assistant aligns with responsible AI standards.
Why Use the Content Safety Evaluator?
Targeted Detection: It identifies language attacking or discriminating against people based on race, religion, gender, and other identity factors.
Severity Scoring: It provides a numerical severity score (often 0-7) to help you understand the level of risk in a response.
Reasoning: It often includes a "reason" or "explanation" column that explains why a specific response was flagged.
Multilingual Support: These models are trained to work across multiple languages, including English, Spanish, French, and Japanese.
Reference:
https://arize.com/blog/evaluating-and-improving-ai-agents-at-scale-with-microsoft-foundry


NEW QUESTION # 64
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 work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?

Answer: A

Explanation:
The Evaluation experience in Microsoft Foundry ' s prompt flow editor shows aggregate quality scores across prompt variants - which variant produces more coherent answers, which scores higher on groundedness.
However, it does not expose per-run raw telemetry: individual token counts per call, per-request latency in milliseconds, or the exact input-output pairs for each execution. The Evaluation experience is designed for comparative quality scoring, not for detailed operational telemetry. To capture inputs, outputs, token usage, and latencies at the granular run level, Tracing must be enabled in Microsoft Foundry. Tracing records each LLM call as a structured span with timing, token consumption, and the complete input-output payload - a fundamentally different view than evaluation scores that directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Trace and debug prompt flows in Microsoft Foundry - Tracing vs.
Evaluation


NEW QUESTION # 65
Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

Answer: C

Explanation:
A model registry provides centralized management of models, including versioning, lineage tracking, and governance. This enables teams to discover, share, and reuse models efficiently.
Simple storage solutions like blob storage lack these advanced capabilities and do not support proper lifecycle management.


NEW QUESTION # 66
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