easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team uses SageMaker notebooks to…
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?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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.
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.
- ✗
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.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.