- A
The evaluation step must output a JSON file in a specific format to be used by the condition step.
SageMaker Pipelines expects the evaluation metrics in a JSON file for condition evaluation.
- B
The condition step can reference the accuracy value using a pipeline parameter or property file.
ConditionStep uses property files or parameters to get values from previous steps.
- C
The conditional step should be implemented as a separate Lambda function called from the pipeline.
Why wrong: SageMaker Pipelines has a native ConditionStep, no need for Lambda.
- D
The pipeline will automatically retry the training step if the condition fails.
Why wrong: SageMaker Pipelines does not automatically retry steps based on conditions; it will fail the pipeline.
- E
The model registration step should be placed before the condition step to ensure the model is always registered.
Why wrong: Registration should only happen if condition is met; placing before would register even if accuracy fails.
Quick Answer
The answer is that the condition step can reference the accuracy value using a pipeline parameter or property file. This is correct because SageMaker Pipelines conditional step execution relies on property files to extract metrics from JSON output produced by a previous step, such as the evaluation step. The condition step reads the property file to retrieve the accuracy value and then evaluates the boolean expression, allowing the pipeline to branch—registering the model if accuracy exceeds 0.9 or failing if it does not. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to wire step outputs into conditional logic, a common trap being that candidates mistakenly think the condition step can parse raw JSON directly without a property file. Remember the key pattern: evaluation step writes a JSON property file, condition step reads it via a PropertyFile object. A useful memory tip is "JSON to property, condition to branch"—the property file is the bridge between metric output and decision logic.
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 team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)
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
The evaluation step must output a JSON file in a specific format to be used by the condition step.
Option A is correct because the SageMaker Pipelines condition step expects the evaluation step to output a JSON file with a specific format, typically containing a metrics dictionary. The condition step then uses a property file to extract the accuracy value from that JSON, enabling the conditional logic to evaluate whether accuracy > 0.9.
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.
- ✓
The evaluation step must output a JSON file in a specific format to be used by the condition step.
Why this is correct
SageMaker Pipelines expects the evaluation metrics in a JSON file for condition evaluation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The condition step can reference the accuracy value using a pipeline parameter or property file.
Why this is correct
ConditionStep uses property files or parameters to get values from previous steps.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The conditional step should be implemented as a separate Lambda function called from the pipeline.
Why it's wrong here
SageMaker Pipelines has a native ConditionStep, no need for Lambda.
- ✗
The pipeline will automatically retry the training step if the condition fails.
Why it's wrong here
SageMaker Pipelines does not automatically retry steps based on conditions; it will fail the pipeline.
- ✗
The model registration step should be placed before the condition step to ensure the model is always registered.
Why it's wrong here
Registration should only happen if condition is met; placing before would register even if accuracy fails.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the built-in ConditionStep with a Lambda-based custom step, or assume that pipeline failure triggers automatic retries, when in fact SageMaker Pipelines requires explicit retry policies and does not retry on condition failures.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Pipelines ConditionStep uses a property file to read the evaluation metrics from the JSON output, and the expression is evaluated using a JSONPath-like syntax. In real-world scenarios, teams often combine this with a ModelQualityCheck step from SageMaker Model Monitor to enforce stricter governance, ensuring only models meeting performance criteria are registered in the Model Registry.
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: The evaluation step must output a JSON file in a specific format to be used by the condition step. — Option A is correct because the SageMaker Pipelines condition step expects the evaluation step to output a JSON file with a specific format, typically containing a metrics dictionary. The condition step then uses a property file to extract the accuracy value from that JSON, enabling the conditional logic to evaluate whether accuracy > 0.9.
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
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.
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