MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

⚠ Common exam trap

MLA-C01 often tests event-driven vs scheduled automation, tricking candidates into choosing polling or scheduled solutions when the requirement is to react to new data arrival.

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

✓

EventBridge -> Lambda -> SageMaker Training Job

EventBridge can capture S3 events (via CloudTrail or S3 event notifications routed to EventBridge) and trigger a Lambda function that starts a SageMaker Training Job. This event-driven pattern is the standard AWS-native way to automate retraining when new data lands in S3. It avoids polling and integrates cleanly with MLflow tracking on SageMaker.

Answer analysis

Option-by-option breakdown

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

✗
SageMaker Pipelines scheduled trigger every hour
Why it's wrong here
Hourly polling ignores the event itself, so retraining lags data arrival and fires needlessly when nothing changed. Event-driven invocation is required here. Scheduled triggers suit recurring cadences, such as nightly retraining on a fixed dataset refresh, where no arrival signal exists to react to.
✓
EventBridge -> Lambda -> SageMaker Training Job
Why this is correct
EventBridge detects the new S3 object and triggers a Lambda function that starts a SageMaker Training Job, giving event-driven retraining. This satisfies the automation requirement, since S3 alone cannot invoke training and Lambda supplies the orchestration glue.
✗
S3 Event Notifications -> SQS -> SageMaker Training Job
Why it's wrong here
SQS decouples but does not invoke anything; a consumer must poll the queue and start training, so the chain is incomplete. S3 Event Notifications to EventBridge or Lambda would trigger directly. SQS is right when buffering bursts or absorbing downstream throttling before a separate compute consumer processes them.
✗
AWS Step Functions with S3 poller
Why it's wrong here
An S3 poller checks the bucket on a schedule, so retraining starts only after the polling interval and misses event-driven immediacy. It is tempting as a simple scheduled trigger, but the correct combination uses S3 event notifications to EventBridge, invoking Step Functions the moment training data arrives.

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 Deployment and Orchestration of ML Workflows Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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