mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker Pipelines to…
A company uses Amazon SageMaker Pipelines to automate its ML workflow. The pipeline includes a training step and a model evaluation step. If the evaluation step fails, the pipeline should stop and notify the team. How should the company configure the pipeline?
⚠ Common exam trap
Many candidates confuse SageMaker Pipelines' built-in conditional branching (ConditionStep) with external monitoring services like Model Monitor or Step Functions, assuming that pipeline failures must be handled outside the pipeline itself.
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
✓
Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.
SageMaker Pipelines natively supports a ConditionStep that can evaluate a metric (e.g., model accuracy) and branch the pipeline execution. By configuring the ConditionStep to check if the evaluation metric falls below a threshold, you can explicitly fail the pipeline and trigger a notification (e.g., via SNS) when the condition is not met. This is the idiomatic, pipeline-native way to halt execution on evaluation failure without external dependencies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.
Why this is correct
A ConditionStep can be used to evaluate metrics and fail the pipeline if conditions are not met.
- ✗
Use Amazon SageMaker Model Monitor to detect failures in the evaluation step.
Why it's wrong here
Model Monitor is for data and model quality drift after deployment, not for pipeline step failure handling.
- ✗
Create an AWS Step Function state machine that monitors the pipeline and stops it on failure.
Why it's wrong here
Step Functions is not the native way to handle pipeline step failures; SageMaker Pipelines has built-in mechanisms.
- ✗
Configure an Amazon CloudWatch alarm on the evaluation step's execution time to stop the pipeline.
Why it's wrong here
CloudWatch alarms do not directly interact with pipeline execution.
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.