- A
Set the 'caching' flag to 'True' in the pipeline definition using 'pipeline.caching = True'.
Why wrong: There is no such pipeline attribute; caching is per-component and controlled via the component decorator options.
- B
Set the environment variable 'ENABLE_CACHE' to 'true' on the pipeline run request.
Why wrong: There is no such environment variable; caching is controlled by the component definition or pipeline run request parameter.
- C
Re-compile the pipeline with the '--enable-cache' flag.
Why wrong: Compilation does not have a caching flag; caching is a runtime feature.
- D
Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set.
Caching is enabled by default; if someone explicitly disabled it, removing that line will re-enable caching.
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 has a Vertex AI pipeline that includes a container component for data preprocessing. The team notices that the component is re-executed every time the pipeline runs, even when the inputs and code haven't changed. They want to leverage pipeline caching to avoid redundant executions. What should they do to enable caching for this component?
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
Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set.
Option D is correct because Vertex AI pipeline caching is enabled by default for all components unless explicitly disabled using `dsl.cache_options(enable_cache=False)`. The component re-executing every time indicates that caching was likely disabled on that specific component. Removing or ensuring this setting is not present will allow the pipeline to reuse cached outputs when inputs and code have not 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.
- ✗
Set the 'caching' flag to 'True' in the pipeline definition using 'pipeline.caching = True'.
Why it's wrong here
There is no such pipeline attribute; caching is per-component and controlled via the component decorator options.
- ✗
Set the environment variable 'ENABLE_CACHE' to 'true' on the pipeline run request.
Why it's wrong here
There is no such environment variable; caching is controlled by the component definition or pipeline run request parameter.
- ✗
Re-compile the pipeline with the '--enable-cache' flag.
Why it's wrong here
Compilation does not have a caching flag; caching is a runtime feature.
- ✓
Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set.
Why this is correct
Caching is enabled by default; if someone explicitly disabled it, removing that line will re-enable caching.
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 assume caching must be explicitly enabled (like in some other systems), but Vertex AI caches by default, so the issue is usually that caching was explicitly disabled on the component.
Detailed technical explanation
How to think about this question
Vertex AI pipeline caching works by computing a hash of the component's image, code, inputs, and dependencies. If the hash matches a previous execution, the cached output is used. This caching is enabled by default for all components, but can be explicitly disabled using `dsl.cache_options(enable_cache=False)` on a component or task. A common real-world scenario is when a component has side effects (e.g., writing to an external database) where caching would incorrectly skip execution, so teams disable it intentionally.
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 PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set. — Option D is correct because Vertex AI pipeline caching is enabled by default for all components unless explicitly disabled using `dsl.cache_options(enable_cache=False)`. The component re-executing every time indicates that caching was likely disabled on that specific component. Removing or ensuring this setting is not present will allow the pipeline to reuse cached outputs when inputs and code have not changed.
What should I do if I get this PMLE 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 PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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