MLA-C01 Practice Question: A data scientist trained a model using SageMaker…

A data scientist trained a model using SageMaker and wants to automate the retraining process when new data becomes available. Which AWS service is best suited to trigger a SageMaker training job based on an S3 event?

⚠ Common exam trap

A common mix-up: candidates confuse scheduled triggers (Step Functions) with event-driven triggers, overlooking that EventBridge is the native AWS service for reacting to S3 events in real time without polling or custom scripts.

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

✓

Amazon EventBridge with a rule matching S3 object creation.

Amazon EventBridge is the correct choice because it can directly capture S3 events (such as ObjectCreated) via a rule and invoke a SageMaker training job as a target. This event-driven architecture eliminates the need for polling or scheduled checks, enabling immediate retraining when new data arrives in S3.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
AWS Step Functions with a scheduled trigger.
Why it's wrong here
A scheduled trigger fires on a timetable, not on S3 object creation, so it cannot react when new data lands. It is tempting because Step Functions orchestrates training well, and would be the right choice if retraining ran on a fixed cadence rather than an event.
✗
Amazon Simple Workflow Service (SWF) decider.
Why it's wrong here
An SWF decider coordinates multi-step workflows by polling for decision tasks, but it cannot directly react to an S3 event to launch a SageMaker training job; S3 event notifications integrate natively with Amazon EventBridge or SNS, not with SWF deciders. This option is tempting because SWF is designed for orchestrating complex, human-involved processes, and a decider could theoretically manage a retraining pipeline if custom polling logic were added. However, the stem requires event-driven automation from S3, which SWF lacks built-in support for, making Amazon EventBridge the correct service for this trigger.
✓
Amazon EventBridge with a rule matching S3 object creation.
Why this is correct
EventBridge rules can match S3 object-created events and invoke a target that starts the training job, giving event-driven retraining without polling. This satisfies the requirement to trigger SageMaker training automatically whenever new data lands in the bucket.
✗
Amazon Simple Queue Service (SQS) with a polling script.
Why it's wrong here
SQS stores messages but has no native S3 event integration; a polling script adds latency and custom code to detect uploads. It is tempting as a decoupling pattern, and would suit buffering training requests, but S3 events must first be routed to a queue via notification configuration.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

MLA-C01 Practice Question: A data scientist trained a model using SageMaker…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question