- A
S3 + SQS + SageMaker Batch Transform
Why wrong: Batch transform is for inference, not training.
- B
S3 + Step Functions + SageMaker Training Job
Why wrong: Step Functions can orchestrate but the ML workflow is better managed by SageMaker Pipeline; Step Functions would require custom integration.
- C
S3 + Lambda + SageMaker Pipeline
S3 event notifies Lambda, which starts a SageMaker Pipeline for the ML workflow.
- D
S3 + EventBridge + SageMaker Model Registry
Why wrong: Model Registry alone cannot execute the training pipeline; it requires a Pipeline or Step Functions.
Question 228 of 1,000
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 implement an event-driven retraining pipeline that triggers retraining when new data arrives in an S3 bucket. The pipeline should include preprocessing, training, evaluation, and conditional registration. Which AWS services should they combine?
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
✓
S3 + Lambda + SageMaker Pipeline
Option C is correct because S3 triggers a Lambda function on new data arrival, which invokes SageMaker Pipeline for the full ML workflow (preprocessing, training, evaluation, and conditional registration). SageMaker Pipeline natively supports conditional steps for model registration, making it the only option that directly satisfies the conditional registration requirement without additional custom logic.
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.
- ✗
S3 + SQS + SageMaker Batch Transform
Why it's wrong here
Batch transform is for inference, not training.
- ✗
S3 + Step Functions + SageMaker Training Job
Why it's wrong here
Step Functions can orchestrate but the ML workflow is better managed by SageMaker Pipeline; Step Functions would require custom integration.
- ✓
S3 + Lambda + SageMaker Pipeline
Why this is correct
S3 event notifies Lambda, which starts a SageMaker Pipeline for the ML workflow.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
S3 + EventBridge + SageMaker Model Registry
Why it's wrong here
Model Registry alone cannot execute the training pipeline; it requires a Pipeline or Step Functions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that Step Functions or EventBridge can replace SageMaker Pipeline's native conditional registration, but only SageMaker Pipeline provides a built-in ConditionStep that directly integrates with Model Registry for gated registration.
Detailed technical explanation
How to think about this question
SageMaker Pipeline uses a directed acyclic graph (DAG) of steps, including ProcessingStep for preprocessing, TrainingStep for training, EvaluationStep for model evaluation (e.g., using a built-in or custom image), and ConditionStep to gate registration in Model Registry based on metrics like accuracy. The pipeline is defined via the SageMaker SDK and executed as a single unit, ensuring idempotency and lineage tracking. In a real-world scenario, this pattern is critical for compliance-heavy industries where model registration must be automatically gated by evaluation thresholds.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: S3 + Lambda + SageMaker Pipeline — Option C is correct because S3 triggers a Lambda function on new data arrival, which invokes SageMaker Pipeline for the full ML workflow (preprocessing, training, evaluation, and conditional registration). SageMaker Pipeline natively supports conditional steps for model registration, making it the only option that directly satisfies the conditional registration requirement without additional custom logic.
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
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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