- A
Use SageMaker Training steps with checkpointing
Why wrong: Checkpointing is for resuming training jobs, not for caching preprocessing outputs.
- B
Use SageMaker Processing steps with caching
Caching in SageMaker Pipelines reuses step outputs when inputs are identical, avoiding redundant computation.
- C
Use SageMaker Feature Store to store the preprocessed features
Why wrong: Feature Store is for online feature serving, not for caching intermediate pipeline step outputs.
- D
Use SageMaker Data Wrangler for the preprocessing
Why wrong: Data Wrangler creates preprocessing flows but caching is handled by Pipelines, not Data Wrangler itself.
Question 684 of 1,000
hardMultiple ChoiceObjective-mapped
Caching Preprocessing Steps in SageMaker Pipelines
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 data science team is using Amazon SageMaker Pipelines to orchestrate a multi-step workflow that includes data preprocessing, training, and model evaluation. They want to reuse the preprocessed data across multiple pipeline executions without re-running the preprocessing step if the source data hasn't changed. What should they configure?
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 SageMaker Processing steps with caching
Option B is correct because SageMaker Processing steps support caching, which allows the pipeline to skip re-execution of the preprocessing step if the input data and pipeline parameters have not changed. This is achieved by configuring a `CacheConfig` with a caching key based on the input data source and step parameters, ensuring that the preprocessed data is reused across multiple pipeline executions without redundant computation.
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.
- ✗
Use SageMaker Training steps with checkpointing
Why it's wrong here
Checkpointing is for resuming training jobs, not for caching preprocessing outputs.
- ✓
Use SageMaker Processing steps with caching
Why this is correct
Caching in SageMaker Pipelines reuses step outputs when inputs are identical, avoiding redundant computation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Feature Store to store the preprocessed features
Why it's wrong here
Feature Store is for online feature serving, not for caching intermediate pipeline step outputs.
- ✗
Use SageMaker Data Wrangler for the preprocessing
Why it's wrong here
Data Wrangler creates preprocessing flows but caching is handled by Pipelines, not Data Wrangler itself.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse checkpointing (for training resumption) with caching (for step reuse), or assume that Feature Store or Data Wrangler inherently provide caching, when in fact only Processing steps with explicit CacheConfig enable this behavior in SageMaker Pipelines.
Trap categories for this question
Command / output trap
Checkpointing is for resuming training jobs, not for caching preprocessing outputs.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Pipelines caching uses a hash of the step's input data (e.g., S3 URI), algorithm specification, and hyperparameters to determine if the step can be skipped. If the hash matches a previous execution, the pipeline reuses the cached output artifacts, which is particularly useful in scenarios like daily retraining pipelines where source data changes infrequently, reducing cost and execution time. A subtle behavior is that caching is only effective if the input data path remains the same; if the source data is overwritten in place, the hash may not change, leading to stale results.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use SageMaker Processing steps with caching — Option B is correct because SageMaker Processing steps support caching, which allows the pipeline to skip re-execution of the preprocessing step if the input data and pipeline parameters have not changed. This is achieved by configuring a `CacheConfig` with a caching key based on the input data source and step parameters, ensuring that the preprocessed data is reused across multiple pipeline executions without redundant computation.
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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