The answer is Amazon SageMaker Pipelines and Amazon SageMaker Model Registry. This combination is correct because SageMaker Pipelines provides the orchestration framework needed to build an automated retraining pipeline with conditional model promotion, including weekly scheduling and the logic to evaluate performance thresholds, while SageMaker Model Registry handles versioning, approval status, and the promotion gate that prevents a new model from reaching production unless it outperforms the current one. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to separate orchestration from model governance—a common trap is confusing SageMaker Pipelines with SageMaker Studio or assuming a single service handles both scheduling and approval. Remember the memory tip: “Pipelines for the process, Registry for the gate.”
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
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?
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
Option D is correct because 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.
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.
✗
Amazon SageMaker Debugger and Amazon SageMaker Clarify
Why it's wrong here
Debugger for training issues, Clarify for bias.
✗
Amazon SageMaker Model Monitor and Amazon SageMaker Ground Truth
Why it's wrong here
Model Monitor for data quality, Ground Truth for labeling, not for conditional promotion.
✗
Amazon SageMaker Autopilot and Amazon SageMaker Experiments
Why it's wrong here
Autopilot automates model building, Experiments for tracking.
✓
Amazon SageMaker Pipelines and Amazon SageMaker Model Registry
Why this is correct
Pipelines orchestrate the workflow, Model Registry manages model versions and approvals.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Pipelines uses a directed acyclic graph (DAG) of steps (e.g., training, evaluation, condition) where a ConditionStep can compare metrics (e.g., F1 score) against a threshold and branch to either register the model in the Model Registry or skip promotion. The Model Registry stores model versions with metadata (e.g., approval status, metrics) and integrates with CI/CD systems via the AWS SDK, enabling automated deployment only when the model status is 'Approved'.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Pipelines and Amazon SageMaker Model Registry — Option D is correct because 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.
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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Question Discussion
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