The answer is a SageMaker Processing step for evaluation, a Model Registry approval step, and a Condition step. The Processing step runs custom evaluation scripts to compute metrics like accuracy or F1 score, while the Condition step checks those metrics against a predefined threshold. The Model Registry approval step then gates deployment by setting the model’s approval status to Approved or Rejected based on the Condition step’s outcome, ensuring only validated models proceed. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of how SageMaker Pipelines orchestrates evaluation as a gating mechanism—a common trap is forgetting the Condition step, thinking the Processing step alone triggers approval. Remember the sequence: Process, Check, Approve. A useful mnemonic is “PCA” (Process, Condition, Approve) to recall the three essential components for model evaluation in SageMaker Pipelines.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
A company uses SageMaker Pipelines for model training and wants to incorporate model evaluation before deployment into production. Which THREE components are essential? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A model registry approval step
A model registry approval step is essential because it gates the deployment of a model based on its evaluation results. In SageMaker Pipelines, you register the model to the Model Registry after training, and the approval status (e.g., Approved or Rejected) determines whether downstream deployment steps execute. This ensures only models meeting quality thresholds are promoted to production.
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.
✓
A model registry approval step
Why this is correct
Approval step creates a model version with approval status to gate deployment.
Related concept
Read the scenario before looking for a memorised answer.
✗
A batch transform step for evaluation
Why it's wrong here
Batch transform can evaluate but is less typical; processing step is more common.
✓
A condition step in the pipeline
Why this is correct
Condition step compares evaluation metrics against thresholds to decide approval.
Related concept
Read the scenario before looking for a memorised answer.
✗
A human review step
Why it's wrong here
Human review is optional, not essential for automated evaluation.
✓
A SageMaker Processing step for evaluation
Why this is correct
Processing step runs custom evaluation scripts on test data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse batch transform (used for inference) with model evaluation (which requires a Processing step to compute metrics), and they overlook that a condition step is the core decision-making component, not a human review step.
Detailed technical explanation
How to think about this question
Under the hood, the condition step in SageMaker Pipelines evaluates a JSONPath expression against the output of a Processing step (e.g., a metric like accuracy > 0.9). If the condition passes, the pipeline proceeds to register the model with an 'Approved' status in the Model Registry; otherwise, it can branch to a failure path. This pattern is critical for MLOps automation, enabling continuous integration and delivery (CI/CD) for ML models without human gatekeeping.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A model registry approval step — A model registry approval step is essential because it gates the deployment of a model based on its evaluation results. In SageMaker Pipelines, you register the model to the Model Registry after training, and the approval status (e.g., Approved or Rejected) determines whether downstream deployment steps execute. This ensures only models meeting quality thresholds are promoted to production.
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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