- A
Use AWS Step Functions to poll the labeling job status and then start training.
Why wrong: Polling is inefficient and adds latency compared to event-driven approach.
- B
Configure an S3 event notification on the labeling job output bucket to trigger a Lambda function that starts training.
Why wrong: S3 events are not directly tied to labeling job completion status; there is a delay and potential race condition.
- C
Use Amazon CloudWatch Events (EventBridge) to detect the completed labeling job and trigger a SageMaker Pipeline execution.
EventBridge directly supports SageMaker events and can start a pipeline execution with minimal latency.
- D
Set up a scheduled cron job in EventBridge to check for completed labeling jobs every hour and start training if found.
Why wrong: Scheduling introduces latency and is not event-driven.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company uses SageMaker Ground Truth to create a labeled dataset, then trains a model using SageMaker Training. They want to automate the pipeline so that whenever a labeling job is completed, it triggers the training job. Which architecture meets this requirement with minimal latency?
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
Use Amazon CloudWatch Events (EventBridge) to detect the completed labeling job and trigger a SageMaker Pipeline execution.
Option C is correct because Amazon EventBridge can natively capture SageMaker job state changes (e.g., `SageMaker Labeling Job State Change` to `Completed`) and directly trigger a SageMaker Pipeline execution. This event-driven approach eliminates polling overhead and provides the lowest latency by reacting immediately when the labeling job finishes.
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.
- ✗
Use AWS Step Functions to poll the labeling job status and then start training.
Why it's wrong here
Polling is inefficient and adds latency compared to event-driven approach.
- ✗
Configure an S3 event notification on the labeling job output bucket to trigger a Lambda function that starts training.
Why it's wrong here
S3 events are not directly tied to labeling job completion status; there is a delay and potential race condition.
- ✓
Use Amazon CloudWatch Events (EventBridge) to detect the completed labeling job and trigger a SageMaker Pipeline execution.
Why this is correct
EventBridge directly supports SageMaker events and can start a pipeline execution with minimal latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set up a scheduled cron job in EventBridge to check for completed labeling jobs every hour and start training if found.
Why it's wrong here
Scheduling introduces latency and is not event-driven.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume S3 event notifications are the simplest event-driven trigger, but they overlook the fact that S3 events can fire on intermediate writes (e.g., partial output files) rather than waiting for the labeling job's definitive `Completed` state, leading to data integrity issues.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker emits detailed CloudWatch Events (EventBridge) for each job state transition, including `SageMaker Labeling Job State Change` with a `status` field of `Completed`. EventBridge rules can match this exact pattern and invoke a SageMaker Pipeline execution via the `StartPipelineExecution` API, ensuring near-instantaneous triggering. In real-world scenarios, this pattern is critical for high-throughput ML pipelines where labeling jobs complete at irregular intervals and any delay in training start time directly impacts model iteration speed.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Deployment and Orchestration of ML Workflows — study guide chapter
Learn the concepts, then practise the questions
- →
Deployment and Orchestration of ML Workflows practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use Amazon CloudWatch Events (EventBridge) to detect the completed labeling job and trigger a SageMaker Pipeline execution. — Option C is correct because Amazon EventBridge can natively capture SageMaker job state changes (e.g., `SageMaker Labeling Job State Change` to `Completed`) and directly trigger a SageMaker Pipeline execution. This event-driven approach eliminates polling overhead and provides the lowest latency by reacting immediately when the labeling job finishes.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.