- A
Configure an S3 event notification to trigger an AWS Step Functions state machine that runs a SageMaker Pipeline.
Step Functions orchestrates training and model registration serverlessly, triggered by new data.
- B
Use AWS Glue to detect new data and trigger a SageMaker training job via a Lambda function.
Why wrong: Glue is more suited for ETL, adding complexity for simple event-driven training.
- C
Write a Python script that runs on a scheduled EC2 instance to check S3 for new data and trigger training.
Why wrong: Managing EC2 instances adds maintenance overhead and is not serverless.
- D
Use Amazon EventBridge to schedule a SageMaker training job every hour, regardless of whether new data exists.
Why wrong: Scheduled training does not respond to new data events and wastes compute when no new data.
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 data science team uses SageMaker notebooks to develop models. They want to automate the process of training and registering models whenever new data arrives in an S3 bucket. The team has limited DevOps experience and needs a solution that requires minimal maintenance. Which approach should the team use?
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 an S3 event notification to trigger an AWS Step Functions state machine that runs a SageMaker Pipeline.
Option A is correct because S3 event notifications can directly trigger an AWS Step Functions state machine, which orchestrates a SageMaker Pipeline to automate model training and registration when new data arrives. This serverless approach requires minimal maintenance and aligns with the team's limited DevOps experience, as Step Functions handles retries, error handling, and workflow coordination without custom infrastructure.
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 an S3 event notification to trigger an AWS Step Functions state machine that runs a SageMaker Pipeline.
Why this is correct
Step Functions orchestrates training and model registration serverlessly, triggered by new data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Glue to detect new data and trigger a SageMaker training job via a Lambda function.
Why it's wrong here
Glue is more suited for ETL, adding complexity for simple event-driven training.
- ✗
Write a Python script that runs on a scheduled EC2 instance to check S3 for new data and trigger training.
Why it's wrong here
Managing EC2 instances adds maintenance overhead and is not serverless.
- ✗
Use Amazon EventBridge to schedule a SageMaker training job every hour, regardless of whether new data exists.
Why it's wrong here
Scheduled training does not respond to new data events and wastes compute when no new data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a scheduled approach (Option D) or a Lambda-based trigger (Option B) because they seem simpler, but the exam tests the ability to select the fully managed, event-driven orchestration (Step Functions + SageMaker Pipeline) that minimizes operational burden while ensuring conditional execution based on new data.
Detailed technical explanation
How to think about this question
Under the hood, S3 event notifications send a JSON payload to EventBridge or directly to Step Functions via a supported target; the state machine can then invoke a SageMaker Pipeline execution using the `arn:aws:states:::sagemaker:createPipelineExecution` integration. A subtle behavior is that S3 event notifications are typically delivered within seconds but are 'best-effort' — for critical workflows, consider enabling S3 Event Notifications with a dead-letter queue (DLQ) in Step Functions to handle missed events. In real-world scenarios, this pattern is ideal for continuous integration/continuous deployment (CI/CD) of ML models where data arrives unpredictably, such as in IoT sensor streams or financial transaction logs.
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.
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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 an S3 event notification to trigger an AWS Step Functions state machine that runs a SageMaker Pipeline. — Option A is correct because S3 event notifications can directly trigger an AWS Step Functions state machine, which orchestrates a SageMaker Pipeline to automate model training and registration when new data arrives. This serverless approach requires minimal maintenance and aligns with the team's limited DevOps experience, as Step Functions handles retries, error handling, and workflow coordination without custom infrastructure.
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
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.
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