- A
Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.
A ConditionStep can be used to evaluate metrics and fail the pipeline if conditions are not met.
- B
Use Amazon SageMaker Model Monitor to detect failures in the evaluation step.
Why wrong: Model Monitor is for data and model quality drift after deployment, not for pipeline step failure handling.
- C
Create an AWS Step Function state machine that monitors the pipeline and stops it on failure.
Why wrong: Step Functions is not the native way to handle pipeline step failures; SageMaker Pipelines has built-in mechanisms.
- D
Configure an Amazon CloudWatch alarm on the evaluation step's execution time to stop the pipeline.
Why wrong: CloudWatch alarms do not directly interact with pipeline execution.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
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.
Option A is correct because 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.
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.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, a ConditionStep in SageMaker Pipelines uses a JSONPath expression to compare a property (e.g., the evaluation metric from a previous step's output) against a threshold. If the condition evaluates to false, the pipeline transitions to a 'Fail' step, which immediately stops all downstream steps and marks the pipeline execution as failed. This approach avoids polling or external triggers, ensuring sub-second reaction to failure conditions. In real-world scenarios, teams often combine this with an SNS topic in the Fail step to send alerts to Slack or email.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold. — Option A is correct because 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.
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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Last reviewed: Jun 24, 2026
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