- A
Use a custom exit handler in the data validation step to abort the pipeline.
Why wrong: Vertex AI Pipelines does not support custom exit handlers; standard mechanisms like on_failure should be used.
- B
Set the 'on_failure' parameter of the data validation component to 'Stop'.
Setting on_failure='Stop' immediately stops the pipeline if the component fails.
- C
Use conditional branches to check the output of data validation before proceeding.
Why wrong: Conditional branches are more complex and not the standard way to stop on failure.
- D
Set the 'cache' option for the preprocessing step to True.
Caching reuses outputs when inputs haven't changed, fulfilling the reuse requirement.
- E
Enable 'skip_if_successful' on the preprocessing step.
Why wrong: There is no 'skip_if_successful' parameter; caching is the correct approach for reusing outputs.
Quick Answer
The correct answer is to set the cache option for the preprocessing step to True and configure the data validation component with on_failure='Stop'. Enabling caching on the preprocessing step allows Vertex AI to reuse outputs from a previous successful run when the source data and parameters remain unchanged, directly addressing the need to avoid redundant computation. Setting on_failure='Stop' on the data validation component ensures the pipeline halts immediately upon a schema mismatch, preventing downstream steps from executing with invalid data. On the Google Professional Machine Learning Engineer exam, this tests your understanding of pipeline failure handling and caching in Vertex AI, a common scenario where candidates mistakenly reach for custom exit handlers or conditional branches. A key trap is confusing caching with skip_if_successful, which is not a standard parameter. Remember the mnemonic: "Cache to reuse, Stop on abuse"—caching reuses unchanged data, while Stop halts on failure.
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 machine learning engineer is designing an ML pipeline on Vertex AI. The pipeline includes multiple steps: data validation, preprocessing, training, evaluation, and deployment. The engineer wants to ensure that if the data validation step fails due to schema mismatch, the pipeline stops immediately and does not proceed. Additionally, they want to reuse the preprocessed data from a previous successful run if the source data hasn't changed. Which two configurations should they use? (Choose two.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"immediately / without restart"Why it matters: Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
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
Set the 'on_failure' parameter of the data validation component to 'Stop'.
Options A and C are correct. Option A: Enabling caching (cache=True) on the preprocessing step allows reuse of outputs when inputs are identical. Option C: Setting on_failure='Stop' on the data validation component stops the pipeline immediately on failure. Option B is wrong because custom exit handlers are not a standard feature. Option D is wrong because 'skip_if_successful' is not a standard parameter; caching is the correct way. Option E is wrong because conditional branches add unnecessary complexity; the on_failure parameter is simpler.
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 a custom exit handler in the data validation step to abort the pipeline.
Why it's wrong here
Vertex AI Pipelines does not support custom exit handlers; standard mechanisms like on_failure should be used.
- ✓
Set the 'on_failure' parameter of the data validation component to 'Stop'.
Why this is correct
Setting on_failure='Stop' immediately stops the pipeline if the component fails.
Clue confirmation
The clue word "immediately / without restart" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use conditional branches to check the output of data validation before proceeding.
Why it's wrong here
Conditional branches are more complex and not the standard way to stop on failure.
- ✓
Set the 'cache' option for the preprocessing step to True.
Why this is correct
Caching reuses outputs when inputs haven't changed, fulfilling the reuse requirement.
Clue confirmation
The clue word "immediately / without restart" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable 'skip_if_successful' on the preprocessing step.
Why it's wrong here
There is no 'skip_if_successful' parameter; caching is the correct approach for reusing outputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
There is no 'skip_if_successful' parameter; caching is the correct approach for reusing outputs.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Automating and orchestrating ML pipelines — study guide chapter
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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: Set the 'on_failure' parameter of the data validation component to 'Stop'. — Options A and C are correct. Option A: Enabling caching (cache=True) on the preprocessing step allows reuse of outputs when inputs are identical. Option C: Setting on_failure='Stop' on the data validation component stops the pipeline immediately on failure. Option B is wrong because custom exit handlers are not a standard feature. Option D is wrong because 'skip_if_successful' is not a standard parameter; caching is the correct way. Option E is wrong because conditional branches add unnecessary complexity; the on_failure parameter is simpler.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "immediately / without restart". Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
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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Last reviewed: Jun 24, 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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