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Question 354 of 507

Deployment and Orchestration of ML Workflows →easyMultiple ChoiceObjective-mapped

Quick Answer

The answer is to use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure. This is correct because SageMaker Pipelines includes a built-in Condition step that evaluates a boolean expression—such as whether evaluation metrics meet a threshold—and then directs execution to different branches. If the condition fails, you can route the pipeline to a Fail step, which immediately stops the pipeline and marks it as failed, providing native, event-driven conditional pipeline failure handling without needing external services. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of native pipeline control flow versus workarounds like Lambda or Step Functions; a common trap is choosing a manual error-checking approach instead of the Condition step’s declarative routing. Memory tip: think of the Condition step as a “gatekeeper” that either lets the pipeline proceed or slams the door shut with a Fail step.

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 is using SageMaker Pipelines to automate a multi-step ML workflow. The pipeline includes data preprocessing, training, and model evaluation. The team wants to ensure that if the evaluation step fails, the pipeline stops and sends an alert to the operations team. Which SageMaker Pipelines feature should they use?

Question 1easymultiple choice

Full question →

A
Configure an Amazon CloudWatch Events rule to monitor the pipeline execution status and stop it if the evaluation step fails
Why wrong: CloudWatch Events can't stop a running pipeline; it can only react to state changes.
B
Register the model in the Model Registry only if evaluation passes, and configure the pipeline to stop if registration fails
Why wrong: Model Registry doesn't control pipeline execution flow.
C
Add a Lambda step after the evaluation step that checks the evaluation metrics and sends an SNS notification if the metrics are below a threshold
Why wrong: Lambda can send alerts but cannot stop the pipeline; the pipeline would continue to subsequent steps.
D
Use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure
Condition step allows branching; a Fail step terminates the pipeline and can trigger notifications via SNS.

Full breakdown with real-world context →

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

✓

Use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure

Option D is correct because SageMaker Pipelines provides a built-in Condition step that evaluates a boolean expression (e.g., checking if evaluation metrics meet a threshold) and then routes execution to different steps. If the condition fails, you can direct the pipeline to a Fail step, which immediately stops the pipeline and marks it as failed. This is the native, event-driven way to halt a pipeline based on step output without relying on external services.

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.

✗
Configure an Amazon CloudWatch Events rule to monitor the pipeline execution status and stop it if the evaluation step fails
Why it's wrong here
CloudWatch Events can't stop a running pipeline; it can only react to state changes.
✗
Register the model in the Model Registry only if evaluation passes, and configure the pipeline to stop if registration fails
Why it's wrong here
Model Registry doesn't control pipeline execution flow.
✗
Add a Lambda step after the evaluation step that checks the evaluation metrics and sends an SNS notification if the metrics are below a threshold
Why it's wrong here
Lambda can send alerts but cannot stop the pipeline; the pipeline would continue to subsequent steps.
✓
Use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure
Why this is correct
Condition step allows branching; a Fail step terminates the pipeline and can trigger notifications via SNS.
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 often confuse external monitoring (CloudWatch) or post-step actions (Lambda) with native pipeline control flow, missing that SageMaker Pipelines has a dedicated Condition step for conditional branching and halting execution.

Detailed technical explanation

How to think about this question

The Condition step in SageMaker Pipelines uses a JSONPath expression to compare values from previous step outputs (e.g., evaluation metrics) against a specified threshold. Under the hood, the pipeline DAG evaluates the condition and, if false, skips all downstream steps except the Fail step, which immediately transitions the pipeline to a 'Failed' status. In a real-world scenario, this pattern is critical for preventing a poorly performing model from being deployed or registered, saving compute costs and avoiding downstream errors.

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.

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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: Use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure — Option D is correct because SageMaker Pipelines provides a built-in Condition step that evaluates a boolean expression (e.g., checking if evaluation metrics meet a threshold) and then routes execution to different steps. If the condition fails, you can direct the pipeline to a Fail step, which immediately stops the pipeline and marks it as failed. This is the native, event-driven way to halt a pipeline based on step output without relying on external services.

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.

About these practice questions

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Same concept, more angles

1 more ways this is tested on MLA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company uses SageMaker Pipelines to automate model retraining. The pipeline runs daily but sometimes fails due to data quality issues. What is the best design to handle this?

medium

✓ A.Add a data quality check step with Conditional to skip training if data fails.
B.Use SageMaker Debugger to monitor training.
C.Use SageMaker Model Registry to track model versions.
D.Increase the instance size for the training step.

Why A: Option A is correct because SageMaker Pipelines supports a data quality check step that can be integrated with a ConditionStep. If the data quality check fails, the ConditionStep can skip the training step entirely, preventing the pipeline from failing due to bad data. This design ensures the pipeline completes successfully (or exits gracefully) without wasting compute resources on training with invalid data.

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