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MLA-C01 Practice Question: A large enterprise has multiple SageMaker…
A large enterprise has multiple SageMaker endpoints serving models for different business units. Each endpoint uses a separate instance type and scaling policy. The enterprise wants to implement a unified monitoring and logging solution to track endpoint health, latency, and errors across all endpoints. They also want to set up alerts when the error rate exceeds 5% over a 5-minute period. The solution must be centralized and use AWS-native services. Which solution should the team implement?
⚠ Common exam trap
Test-takers frequently confuse SageMaker Model Monitor (data quality) with endpoint monitoring (operational health), or assume CloudWatch Logs are required when SageMaker endpoints already emit rich metrics directly to CloudWatch.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.
Amazon CloudWatch can natively ingest SageMaker endpoint metrics (e.g., 5xx error counts, latency, invocation counts) without additional configuration. By creating a CloudWatch dashboard, you aggregate metrics from all endpoints into a single view, and a composite alarm using the Sum statistic across endpoints over a 5-minute period directly triggers when the error rate exceeds 5%. This approach is fully centralized, uses only AWS-native services, and requires no custom code or data streaming.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable SageMaker Model Monitor data capture on each endpoint and stream captured data to Amazon Kinesis for analysis.
Why it's wrong here
Model Monitor captures inference data for drift, not for real-time error monitoring.
- ✗
Use AWS CloudTrail to audit all API calls to SageMaker and set up alarms on error responses.
Why it's wrong here
CloudTrail audits control plane operations, not inference requests or errors.
- ✗
Use Amazon CloudWatch Logs to collect logs from each endpoint, and use a Lambda function to parse logs and calculate error rates, then publish custom metrics.
Why it's wrong here
This approach adds custom code and complexity compared to using built-in CloudWatch metrics.
- ✓
Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.
Why this is correct
CloudWatch natively aggregates metrics and composite alarms can alert on the combined error rate.
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Senior Network & Security Engineer · founder of Courseiva
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