hardMultiple Choice
MLA-C01 Practice Question: An MLOps engineer is building an automated…
An MLOps engineer is building an automated retraining pipeline for a fraud detection model. The model must be retrained weekly, and the new model should only be promoted to production if it meets predefined performance thresholds compared to the current model. Which combination of SageMaker capabilities should the engineer use?
⚠ Common exam trap
AWS often tests the distinction between monitoring tools (Model Monitor, Debugger) and orchestration/registry services (Pipelines, Model Registry), so the trap here is that candidates may confuse Model Monitor's drift detection with the need for a retraining pipeline, overlooking that the question specifically requires automated retraining and conditional promotion.
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 and Amazon SageMaker Model Registry
Amazon SageMaker Pipelines provides the orchestration for the automated retraining workflow (including weekly scheduling and conditional logic), while SageMaker Model Registry enables versioning, approval, and promotion of models based on performance thresholds. Together, they allow the engineer to define a pipeline that trains a new model, evaluates it against the current production model, and only registers it for deployment if it meets the predefined criteria.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker Debugger and Amazon SageMaker Clarify
Why it's wrong here
Debugger captures training-job tensors and Clarify detects bias and explains predictions; neither compares a new model's metrics against thresholds to gate promotion. Tempting because both support model quality work, they would suit diagnosing training anomalies or auditing fairness rather than automated approval decisions.
- ✗
Amazon SageMaker Model Monitor and Amazon SageMaker Ground Truth
Why it's wrong here
Model Monitor detects drift in deployed endpoints and Ground Truth labels data; neither compares candidate metrics against thresholds before promotion. Tempting because both support production ML quality, they would be right for catching post-deployment drift or building labelled datasets rather than approving a retrained model.
- ✗
Amazon SageMaker Autopilot and Amazon SageMaker Experiments
Why it's wrong here
Autopilot automates model building and Experiments tracks runs; neither evaluates a candidate against the incumbent's thresholds to authorise deployment. Tempting because both fit MLOps workflows, they would be correct for automating feature engineering and comparing experiment trials rather than gating promotion.
- ✓
Amazon SageMaker Pipelines and Amazon SageMaker Model Registry
Why this is correct
SageMaker Pipelines orchestrates the weekly retraining workflow, while Model Registry stores versioned models with approval status. The engineer sets a condition step comparing the new model's metrics against thresholds, and promotion to production occurs only via manual or automated approval in the registry, satisfying the gating requirement.
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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.