The answer is that the small custom service account lacks sufficient quotas or permissions to create the required number of trials or workers. This is the most likely cause because a ResourceExhausted error during Vertex AI hyperparameter tuning directly signals that the service account has hit a hard limit on resource creation, such as the maximum concurrent trials or training workers allowed by its assigned AI Platform quotas. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how Vertex AI Pipelines components inherit the permissions and quota limits of their configured service account, not just the pipeline’s default compute account. A common trap is to assume the error is due to insufficient compute resources or a code bug, but the key clue is the intermittent failure tied to a restricted custom service account. Memory tip: think “quota exhaustion, not code exhaustion”—the error is about what the account is allowed to create, not what the code requests.
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 company is using Vertex AI Pipelines with reusable components. They observe that a component that performs hyperparameter tuning is failing intermittently with a 'ResourceExhausted' error. The component is configured with a small custom service account. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The service account does not have sufficient quotas or permissions to create the required number of trials or workers
The 'ResourceExhausted' error in Vertex AI Pipelines typically indicates that the component is trying to create more resources (e.g., trials or workers for hyperparameter tuning) than allowed by the assigned service account's quotas or permissions. A small custom service account often has restricted quotas for AI Platform services, such as the number of concurrent trials or training workers, leading to this failure.
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 component code has a bug causing infinite recursion
Why it's wrong here
Infinite recursion would cause stack overflow or timeout, not ResourceExhausted typically.
✗
The KFP executor is not properly configured
Why it's wrong here
KFP executor handles component execution; the error is from the AI Platform resource creation.
✓
The service account does not have sufficient quotas or permissions to create the required number of trials or workers
Why this is correct
Hyperparameter tuning often spawns multiple trial jobs; quota limits on AI Platform training jobs or compute resources can cause this error.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The pipeline system memory is insufficient for the component
Why it's wrong here
Memory is allocated per component; tuning jobs use separate resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that 'ResourceExhausted' errors are always due to memory or code bugs, rather than understanding that Vertex AI enforces service-account-specific quotas for hyperparameter tuning resources.
Detailed technical explanation
How to think about this question
Vertex AI hyperparameter tuning uses the AI Platform Training service, which enforces project-level and service-account-level quotas for concurrent trials and training workers. The 'ResourceExhausted' error often corresponds to the 'aiplatform.googleapis.com/tuning_job_trial_count' quota being exceeded. When a custom service account has a low quota limit (e.g., 5 concurrent trials), a pipeline that launches many trials in parallel will fail, even if the component code and pipeline memory are sufficient.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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.
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 service account does not have sufficient quotas or permissions to create the required number of trials or workers — The 'ResourceExhausted' error in Vertex AI Pipelines typically indicates that the component is trying to create more resources (e.g., trials or workers for hyperparameter tuning) than allowed by the assigned service account's quotas or permissions. A small custom service account often has restricted quotas for AI Platform services, such as the number of concurrent trials or training workers, leading to this failure.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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