MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team needs to automatically retrain a model every week using new data. Which SageMaker feature is designed to schedule and automate this workflow?
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 enables building, automating, and scheduling end-to-end ML workflows, making it suitable for weekly retraining. Option A is correct. Option B, SageMaker Automatic Model Tuning, is for hyperparameter optimization, not scheduling. Option C, SageMaker Model Monitor, is for monitoring model quality and drift, not scheduling. Option D, SageMaker Data Wrangler, is for data preparation and feature engineering, not scheduling.
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 define and schedule training workflows.
- ✗
SageMaker Automatic Model Tuning
Why it's wrong here
Tuning finds hyperparameters, not scheduling retraining.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift, does not retrain.
- ✗
SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is for data preparation, not workflow automation.
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