Single Choice Moderate

Q

A company has a customer service agent built in Microsoft Foundry that calls an Azure OpenAI model and a knowledge API. Telemetry and online evaluation scores are sent to Azure Monitor Application Insights.

Users report intermittent off-topic responses, even though latency and token usage remain normal.

You confirm that the issue occurs only when the knowledge API is used.

You need to detect quality regressions in production within five minutes of occurrence and automatically notify the on-call team.

What should you configure?

ID: #26598 Practice Assessment for Exam AB-100: Agentic AI Business Solutions Architect 10 views
Question Info
#26598Q ID
ModerateDifficulty
Practice Assessment for Exam AB-100: Agentic AI Business Solutions ArchitectTopic

Choose the Best Option

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  • A an Azure Monitor workbook that visualizes evaluation scores and knowledge API usage over time ✔ ✖
  • B an Azure Monitor log alert that detects dependency failures on the knowledge API based on HTTP status codes ✔ ✖
  • C an Azure Monitor metric alert in Application Insights based on the average evaluation scores across all requests ✔ ✖
  • D an Azure Monitor log alert in Application Insights that detects low evaluation scores when the knowledge API is used and triggers an action group ✔ ✖
Correct Answer

Explanation

Objective:

3.1 Analyze, monitor, and tune AI-powered business solutions

What This Item Tests:

Interpret telemetry data for performance and model tuning

Additional Reading:

Explore how to monitor with Azure

Monitor and analyze AI Agent usage and performance

Building and operationalizing copilot applications

Rationale:

A log-based alert in Application Insights can correlate low evaluation scores specifically with calls to the knowledge API and triggers an action group within minutes, enabling rapid detection of targeted quality regressions. Metric alerts based on overall averages, dependency alerts based only on HTTP status codes, and workbooks for visualization do not provide precise, automated detection and notification in this scenario.

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