Question 380 of 507
Deployment and Orchestration of ML WorkflowsmediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events. This approach is correct because EventBridge natively captures object-level events from S3 and can directly invoke a SageMaker Pipeline as a target, enabling a fully event-driven, serverless workflow without the need for polling or manual intervention. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of integrating AWS services for automated ML retraining—a common trap is choosing AWS Lambda as an intermediary, but EventBridge provides a simpler, direct integration with fewer moving parts. Remember that EventBridge acts as the central event bus, while SageMaker Pipelines is the target, making the architecture clean and scalable. Memory tip: think "EventBridge triggers, Pipeline runs"—no Lambda needed for simple S3-to-pipeline automation.

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 is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?

Question 1mediummultiple choice
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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

Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events

Option A is correct because Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.

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.

  • Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events

    Why this is correct

    EventBridge can detect S3 events and start pipeline executions.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Register the pipeline as a model package in SageMaker Model Registry

    Why it's wrong here

    Model registry does not trigger pipelines.

  • Configure a cron job to run the pipeline every hour

    Why it's wrong here

    Cron is periodic, not event-driven.

  • Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears

    Why it's wrong here

    While possible, EventBridge is simpler and more efficient.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may overcomplicate the solution by choosing Step Functions (Option D) for orchestration, not realizing that EventBridge provides a simpler, event-driven trigger without the need for polling or additional state machines.

Detailed technical explanation

How to think about this question

Under the hood, Amazon EventBridge uses a default event bus that captures S3 events (e.g., s3:ObjectCreated:Put) and can route them to a SageMaker Pipelines execution via a Lambda function or directly using the SageMaker Pipelines target. This pattern ensures near-real-time triggering, avoids idle compute costs, and integrates with S3 event notifications, which are delivered with at-least-once semantics. In real-world scenarios, this is critical for continuous integration and continuous delivery (CI/CD) of ML models where data freshness directly impacts model accuracy.

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.

What to study next

Got this wrong? Here's your next step.

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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: Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events — Option A is correct because Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.

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: Jun 24, 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.