MLA-C01 Question 405
Select 2You are a Machine Learning Engineer managing an inference endpoint deployed on Amazon SageMaker. Recently, you have received reports that the endpoint is experiencing high latency during inference requests. Which combination of tools would you use to identify and troubleshoot the performance bottleneck?
- A
Use AWS X-Ray to trace requests and identify latency across different services interacting with the endpoint.
- B
Use Amazon CloudWatch Lambda Insights to monitor CPU and memory utilization of the SageMaker endpoint.
- C
Query Amazon CloudWatch Logs Insights for detailed logs of inference requests, error rates, and response times.
- D
Configure a SageMaker endpoint-specific Amazon SNS topic to receive real-time alerts for high latency.
- E
Enable Amazon SageMaker Model Monitor to automatically detect data quality issues contributing to latency.
Show answer and explanation
Correct answers: A, C
Explanation
To troubleshoot latency and performance issues in a SageMaker inference endpoint, AWS X-Ray can trace requests to pinpoint where latency is introduced, while Amazon CloudWatch Logs Insights can help analyze detailed logs to identify patterns or errors contributing to the delays. Combining these tools provides comprehensive observability into the system's behavior.
- A. Correct.
AWS X-Ray allows you to trace requests through multiple services, making it a powerful tool for identifying latency issues in distributed systems, including SageMaker endpoints.
- B. Incorrect.
Amazon CloudWatch Lambda Insights is specifically designed for monitoring AWS Lambda functions, not SageMaker endpoints, so it is not suitable in this context.
- C. Correct.
Amazon CloudWatch Logs Insights provides detailed logs that allow you to query and analyze inference requests, error rates, and response times, making it an effective tool for troubleshooting latency issues.
- D. Incorrect.
Configuring an Amazon SNS topic can send real-time alerts, but it does not help directly in identifying or troubleshooting the root cause of latency issues.
- E. Incorrect.
Amazon SageMaker Model Monitor is used to detect data quality issues like data drift or bias, but it does not directly address latency or performance bottlenecks.