- A
AWS Step Functions with Lambda functions for each step.
Why wrong: Step Functions can orchestrate, but SageMaker Pipelines offers ML-specific features like model evaluation and registry integration.
- B
AWS Glue workflows with triggers based on S3 events.
Why wrong: Glue workflows are for ETL, not full ML pipeline with training and deployment.
- C
AWS CodePipeline with source from S3 and build from CodeBuild.
Why wrong: CodePipeline is for CI/CD of applications, not specialized for ML models.
- D
Amazon SageMaker Pipelines triggered by S3 events via EventBridge.
SageMaker Pipelines is designed for ML workflows and supports S3 event triggers.
Question 866 of 1,000
easyMultiple ChoiceObjective-mapped
Trigger SageMaker Pipelines on S3 Events with EventBridge
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 automate the retraining and deployment of an ML model whenever new labeled data arrives in S3. The workflow includes data preprocessing, training, evaluation, and conditional deployment. Which AWS service is best suited for orchestrating this end-to-end pipeline?
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
✓
Amazon SageMaker Pipelines triggered by S3 events via EventBridge.
Amazon SageMaker Pipelines is purpose-built for ML workflows, offering native integration with SageMaker for training, evaluation, and conditional deployment steps. Triggered by S3 events via Amazon EventBridge, it automates the end-to-end pipeline from data preprocessing to conditional model deployment without requiring custom orchestration code.
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.
- ✗
AWS Step Functions with Lambda functions for each step.
Why it's wrong here
Step Functions can orchestrate, but SageMaker Pipelines offers ML-specific features like model evaluation and registry integration.
- ✗
AWS Glue workflows with triggers based on S3 events.
Why it's wrong here
Glue workflows are for ETL, not full ML pipeline with training and deployment.
- ✗
AWS CodePipeline with source from S3 and build from CodeBuild.
Why it's wrong here
CodePipeline is for CI/CD of applications, not specialized for ML models.
- ✓
Amazon SageMaker Pipelines triggered by S3 events via EventBridge.
Why this is correct
SageMaker Pipelines is designed for ML workflows and supports S3 event triggers.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AWS Step Functions (Option A) because it is a general-purpose orchestrator, but they overlook that SageMaker Pipelines provides tighter integration with ML-specific steps and reduces custom code overhead.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps, including ProcessingStep for preprocessing, TrainingStep for training, ConditionStep for evaluation-based branching, and CreateModelStep/DeployStep for deployment. Under the hood, each step is a SageMaker job (e.g., ProcessingJob, TrainingJob) with automatic artifact tracking and lineage, enabling reproducibility. In a real-world scenario, if the evaluation metric (e.g., accuracy) falls below a threshold, the ConditionStep can skip deployment and trigger a notification, preventing a poor model from reaching production.
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.
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.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
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.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Pipelines triggered by S3 events via EventBridge. — Amazon SageMaker Pipelines is purpose-built for ML workflows, offering native integration with SageMaker for training, evaluation, and conditional deployment steps. Triggered by S3 events via Amazon EventBridge, it automates the end-to-end pipeline from data preprocessing to conditional model deployment without requiring custom orchestration code.
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 →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- ✓ A.Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events
- B.Register the pipeline as a model package in SageMaker Model Registry
- C.Configure a cron job to run the pipeline every hour
- D.Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears
Why A: 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.
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
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.