easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team wants to automatically retrain a model…
A team wants to automatically retrain a model when new labeled data arrives. Which SageMaker feature can orchestrate this workflow?
⚠ Common exam trap
Candidates often confuse monitoring services (Model Monitor, Debugger) with orchestration services, or assume Autopilot's automation includes workflow orchestration, when in fact only Pipelines provides the explicit DAG-based orchestration needed to chain retraining steps on new data events.
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
✓
SageMaker Pipelines
SageMaker Pipelines is a purpose-built CI/CD service for machine learning that allows you to define, orchestrate, and automate end-to-end ML workflows, including retraining models when new labeled data arrives. You can create a pipeline that triggers on new data events (e.g., via an S3 event notification or a Lambda function) and automatically executes steps such as data processing, training, evaluation, and model registration. This makes it the correct choice for orchestrating an automated retraining workflow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker Pipelines
Why this is correct
Pipelines can orchestrate a retraining workflow when triggered.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift, does not orchestrate retraining.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training jobs for issues, not retraining.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model building but not ongoing retraining.
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 |
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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.