- A
The pipeline parameter values have changed, causing a cache hit.
Why wrong: If parameter values change, the cache key changes, so caching should not reuse outputs.
- B
The pipeline is using a pre-built component that ignores caching.
Why wrong: Pre-built components use caching by default; ignoring caching would not cause this issue.
- C
The base image used for the component has not changed, so the cache key matches despite code changes inside the container.
The cache key includes the base image digest; if only the code inside the container changes but the image tag/digest remains the same, the cache key may still match.
- D
The component has caching disabled via the @dsl.component decorator.
Why wrong: Disabling caching would force re-execution, not cause stale cached outputs.
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 machine learning engineer has a Vertex AI pipeline that trains a model. The pipeline uses caching to avoid re-running components that have not changed. After updating the training code, the engineer notices that the pipeline still uses cached outputs from the previous run. What could be the reason?
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 base image used for the component has not changed, so the cache key matches despite code changes inside the container.
In Vertex AI Pipelines, caching uses a cache key derived from the component source code, input parameters, and the base image digest. If only the training code inside the container changes but the base image digest remains the same, the cache key does not change, resulting in a cache hit from the previous run. This explains why the pipeline still uses cached outputs. Option A is incorrect because changing parameter values would change the cache key, causing a miss. Option B is incorrect because pre-built components support caching unless explicitly disabled. Option D would disable caching entirely, which is not the case here.
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 pipeline parameter values have changed, causing a cache hit.
Why it's wrong here
If parameter values change, the cache key changes, so caching should not reuse outputs.
- ✗
The pipeline is using a pre-built component that ignores caching.
Why it's wrong here
Pre-built components use caching by default; ignoring caching would not cause this issue.
- ✓
The base image used for the component has not changed, so the cache key matches despite code changes inside the container.
Why this is correct
The cache key includes the base image digest; if only the code inside the container changes but the image tag/digest remains the same, the cache key may still match.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The component has caching disabled via the @dsl.component decorator.
Why it's wrong here
Disabling caching would force re-execution, not cause stale cached 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
If parameter values change, the cache key changes, so caching should not reuse 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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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: The base image used for the component has not changed, so the cache key matches despite code changes inside the container. — In Vertex AI Pipelines, caching uses a cache key derived from the component source code, input parameters, and the base image digest. If only the training code inside the container changes but the base image digest remains the same, the cache key does not change, resulting in a cache hit from the previous run. This explains why the pipeline still uses cached outputs. Option A is incorrect because changing parameter values would change the cache key, causing a miss. Option B is incorrect because pre-built components support caching unless explicitly disabled. Option D would disable caching entirely, which is not the case here.
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.
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: 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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