What You'll Be Tested On

Monitoring data quality, model quality, bias drift, explainability drift, endpoints, and infrastructure
Using CloudWatch metrics, logs, dashboards, and alarms for ML operations
Troubleshooting failed training jobs, degraded endpoints, and unhealthy pipelines
Securing data, model artifacts, notebooks, training jobs, endpoints, and logs
Applying IAM least privilege, KMS encryption, VPC endpoints, and private connectivity

Key AWS Services in This Domain

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Model Monitor needs a baseline before it can compare production traffic against expected behavior.

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CloudWatch tells you about infrastructure and endpoint health; model quality metrics tell you whether predictions are still useful.

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Use KMS and IAM together: encryption protects data, policies control who can use the data and keys.

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When data drift is confirmed, the next action is often retraining or reviewing the data pipeline.

Test your knowledge of ML Solution Monitoring, Maintenance, and Security with practice questions from our MLA-C01 question bank.