- A
Pipeline caching
Caching reuses step outputs when inputs and configuration haven't changed, avoiding redundant processing.
- B
Model lineage tracking
Why wrong: Lineage tracking records metadata but does not skip steps.
- C
Parameterized pipeline executions
Why wrong: Parameters allow passing different values but do not automatically skip steps.
- D
Step parallelism
Why wrong: Step parallelism runs steps concurrently, but does not skip unchanged steps.
Question 920 of 1,000
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 machine learning team uses SageMaker Pipelines to automate retraining. They want to avoid re-running data processing steps if the data has not changed since the last successful pipeline run. Which built-in feature should they enable?
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
✓
Pipeline caching
Pipeline caching is the correct choice because SageMaker Pipelines can cache the outputs of each step based on a hash of the step's input parameters, configuration, and code. If the hash matches a previous successful run, the cached output is reused, avoiding redundant execution of data processing steps when the underlying data hasn't changed.
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.
- ✓
Pipeline caching
Why this is correct
Caching reuses step outputs when inputs and configuration haven't changed, avoiding redundant processing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Model lineage tracking
Why it's wrong here
Lineage tracking records metadata but does not skip steps.
- ✗
Parameterized pipeline executions
Why it's wrong here
Parameters allow passing different values but do not automatically skip steps.
- ✗
Step parallelism
Why it's wrong here
Step parallelism runs steps concurrently, but does not skip unchanged steps.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse lineage tracking (Option B) with caching, assuming that tracking data versions automatically prevents re-execution, when in fact lineage only records history without affecting pipeline execution behavior.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Pipelines caching uses a deterministic hash computed from the step's input data (e.g., S3 URI, dataset version), the step type, and its configuration parameters. If the hash matches a previously successful execution, the pipeline retrieves the cached output artifact from the default S3 bucket managed by SageMaker, bypassing the compute step entirely. This is especially valuable in production pipelines where data processing steps (e.g., Spark jobs or custom containers) are expensive and time-consuming, and where data updates are infrequent.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: Pipeline caching — Pipeline caching is the correct choice because SageMaker Pipelines can cache the outputs of each step based on a hash of the step's input parameters, configuration, and code. If the hash matches a previous successful run, the cached output is reused, avoiding redundant execution of data processing steps when the underlying data hasn't changed.
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: Jul 4, 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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