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Deployment and Orchestration of ML Workflows →mediumMultiple ChoiceObjective-mapped

MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?

A
Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline
EventBridge triggers the Step Functions workflow upon new data arrival, allowing orchestration of retraining and drift monitoring.
B
Use a cron-based Step Function schedule that checks for new data every hour
Why wrong: Scheduled checks are less efficient than event-driven; they also introduce latency.
C
Set up an S3 event notification to invoke a Lambda function that starts a SageMaker training job directly
Why wrong: While possible, this lacks orchestration capabilities; Step Functions provides better workflow management and error handling.
D
Use SageMaker Pipelines with a schedule trigger
Why wrong: SageMaker Pipelines can be scheduled, but not directly triggered by S3 events without EventBridge.

Full breakdown with real-world context →

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

✓

Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline

Option A is correct because AWS EventBridge can capture S3 PutObject events (via S3's default event notifications or a more granular EventBridge rule) and directly target a Step Functions state machine as a target. This creates a fully event-driven, serverless orchestration for the retraining pipeline without polling or custom code. Step Functions then coordinates the retraining steps, including model drift monitoring, in a reliable and auditable manner.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

✓
Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline
Why this is correct
EventBridge triggers the Step Functions workflow upon new data arrival, allowing orchestration of retraining and drift monitoring.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a cron-based Step Function schedule that checks for new data every hour
Why it's wrong here
Scheduled checks are less efficient than event-driven; they also introduce latency.
✗
Set up an S3 event notification to invoke a Lambda function that starts a SageMaker training job directly
Why it's wrong here
While possible, this lacks orchestration capabilities; Step Functions provides better workflow management and error handling.
✗
Use SageMaker Pipelines with a schedule trigger
Why it's wrong here
SageMaker Pipelines can be scheduled, but not directly triggered by S3 events without EventBridge.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse S3 event notifications (which directly invoke Lambda) with EventBridge (which can target Step Functions), and they overlook that Step Functions is the recommended orchestration service for complex ML workflows, not just Lambda or SageMaker Pipelines alone.

Detailed technical explanation

How to think about this question

Under the hood, EventBridge uses a default event bus that can receive S3 events when you enable S3's 'Amazon EventBridge' integration at the bucket level, which sends structured events with details like bucket name, object key, and size. Step Functions can then use these event details to parameterize the state machine execution, enabling dynamic retraining based on the specific new data file. In real-world scenarios, this pattern is critical for MLOps pipelines where model drift must be detected and retraining triggered within seconds of data arrival, not minutes or hours.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline — Option A is correct because AWS EventBridge can capture S3 PutObject events (via S3's default event notifications or a more granular EventBridge rule) and directly target a Step Functions state machine as a target. This creates a fully event-driven, serverless orchestration for the retraining pipeline without polling or custom code. Step Functions then coordinates the retraining steps, including model drift monitoring, in a reliable and auditable manner.

What should I do if I get this MLA-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.