MLA-C01 Questions Bank

AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.

Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.

Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event-based trigger.

AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.

Therefore, Option C is the correct and AWS-verified answer.

For near real-time predictions, AWS documentation recommends Amazon SageMaker real-time inference endpoints. These endpoints support automatic scaling based on metrics such as ConcurrentInvocationsPerInstance, ensuring low latency and high availability during traffic spikes.

For long-running historical analysis, SageMaker Processing jobs are the appropriate solution. Processing jobs are designed for batch workloads, can run for hours, and integrate cleanly with SageMaker pipelines. Scheduling them with Amazon EventBridge provides a fully managed, scalable, and serverless solution.

AWS Lambda is unsuitable for multi-hour workloads. EC2 Auto Scaling adds unnecessary infrastructure management overhead.

Therefore, Options A and C together meet all requirements and align with AWS best practices.