AI-102 Question 63
Select 3You are managing an Azure AI resource that powers multiple machine learning models in production. Recently, you have noticed a decrease in the performance of one of the deployed models. Which of the following actions can you take to monitor and diagnose issues with the Azure AI resource and the model performance?
- A
Enable Application Insights to track request and response data, latency, and error rates.
- B
Use Azure Monitor to create custom alerts for service health and resource utilization metrics.
- C
Review the model explanation dashboard in Azure Machine Learning to analyze feature importance.
- D
Inspect usage patterns by analyzing logs in Azure Blob Storage.
- E
Enable Diagnostic Settings to send logs and metrics to a Log Analytics workspace for further analysis.
Show answer and explanation
Correct answers: A, B, E
Explanation
To monitor and diagnose Azure AI resource issues, you should leverage tools like Application Insights for telemetry, Azure Monitor for custom alerts and metrics, and Diagnostic Settings to send logs and metrics to a Log Analytics workspace for advanced analysis. These tools collectively provide a comprehensive view of resource performance and health, enabling better diagnosis and resolution of performance issues.
- A. Correct.
Enabling Application Insights provides detailed telemetry data such as latency, error rates, and request-response tracking, which helps in diagnosing performance issues.
- B. Correct.
Azure Monitor allows you to set up alerts and monitor the health, metrics, and logs of your Azure AI resources, enabling proactive issue detection.
- C. Incorrect.
The model explanation dashboard is primarily used to interpret model decisions and feature importance but does not directly help in monitoring resource or system-level performance.
- D. Incorrect.
Analyzing logs in Azure Blob Storage can provide information about data usage but does not actively monitor or diagnose Azure AI resource performance.
- E. Correct.
Enabling Diagnostic Settings allows you to send metrics and logs to a Log Analytics workspace, where you can analyze them to identify potential issues with the resource.