- A
The BigQuery table is partitioned on a date column, and the pipeline is querying a specific partition that exceeds the quota.
Why wrong: Partitioning does not affect API request quota; it reduces data scanned.
- B
The Cloud Scheduler job is triggering multiple pipeline runs that overlap, causing concurrent quota usage.
Why wrong: Overlapping runs could increase concurrent usage, but project-level quota would likely be hit first.
- C
The preprocessing component is using a BigQuery client library that does not use exponential backoff for retries.
Without backoff, rapid retries can exhaust per-user read API quotas.
- D
The pipeline is using a shared VPC that has traffic shaping limits.
Why wrong: Traffic shaping affects network throughput, not API request quotas.
Quick Answer
The answer is that the preprocessing component is using a BigQuery client library that does not implement exponential backoff for retries. This is correct because the "ResourceExhausted: Quota limits exceeded for read api requests" error occurs when the client sends requests too aggressively, triggering per-client rate limiting even when the project-level BigQuery quota is not exhausted. The pipeline’s custom machine type and persistent disk are irrelevant; the root cause is the lack of retry logic in the client code, which causes rapid, repeated read requests that overwhelm the API’s per-connection throttle. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI Pipeline BigQuery quota exhaustion troubleshooting—specifically that API errors often stem from client-side behavior, not project quotas. A common trap is to assume the project quota is the issue, but the exam wants you to recognize that exponential backoff is essential for distributed workloads reading billions of rows. Memory tip: “Backoff beats back the quota block.”
PMLE Practice Question: Collaborating within and across teams to manage data and models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and models. 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 large e-commerce company uses Vertex AI to train a recommendation model daily. The training pipeline is built with Vertex AI Pipelines and involves three steps: data preprocessing, training, and model evaluation. The pipeline is triggered by a Cloud Scheduler job every morning at 8 AM. Recently, the pipeline has been failing intermittently during the data preprocessing step, with an error message indicating 'ResourceExhausted: Quota limits exceeded for read api requests.' The team has checked and confirmed that the quota for BigQuery read requests is not exceeded at the project level. The preprocessing step reads data from a BigQuery table with billions of rows. The team has also noticed that the pipeline runs on a custom machine type (n1-standard-4) with a persistent disk. What is the most likely cause of this error?
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 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 preprocessing component is using a BigQuery client library that does not use exponential backoff for retries.
Option C is correct because the error 'ResourceExhausted: Quota limits exceeded for read api requests' indicates that the BigQuery API is throttling requests from the client, even though the project-level quota is not exceeded. The preprocessing component likely uses a BigQuery client library that lacks exponential backoff retry logic, causing rapid, repeated requests that exhaust the per-client or per-connection quota. Implementing exponential backoff would allow the client to back off and retry, preventing quota exhaustion.
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 BigQuery table is partitioned on a date column, and the pipeline is querying a specific partition that exceeds the quota.
Why it's wrong here
Partitioning does not affect API request quota; it reduces data scanned.
- ✗
The Cloud Scheduler job is triggering multiple pipeline runs that overlap, causing concurrent quota usage.
Why it's wrong here
Overlapping runs could increase concurrent usage, but project-level quota would likely be hit first.
- ✓
The preprocessing component is using a BigQuery client library that does not use exponential backoff for retries.
Why this is correct
Without backoff, rapid retries can exhaust per-user read API quotas.
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 is using a shared VPC that has traffic shaping limits.
Why it's wrong here
Traffic shaping affects network throughput, not API request quotas.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume quota errors always mean the project-level limit is reached, but Cisco tests the nuance that per-client or per-connection rate limits can be exhausted independently, especially when retry logic is missing.
Detailed technical explanation
How to think about this question
BigQuery read API quota is enforced at multiple levels: project, user, and per-request. Even if the project-level quota is not exceeded, a client without exponential backoff can hit a per-client rate limit (e.g., 100 requests per second per user) by sending requests too aggressively. The BigQuery client library's default behavior includes automatic retries with exponential backoff, but if the code uses a lower-level API or disables retries, it can trigger this error. In practice, this often occurs when reading large tables with multiple parallel queries without proper throttling.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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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Collaborating within and across teams to manage data and models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The preprocessing component is using a BigQuery client library that does not use exponential backoff for retries. — Option C is correct because the error 'ResourceExhausted: Quota limits exceeded for read api requests' indicates that the BigQuery API is throttling requests from the client, even though the project-level quota is not exceeded. The preprocessing component likely uses a BigQuery client library that lacks exponential backoff retry logic, causing rapid, repeated requests that exhaust the per-client or per-connection quota. Implementing exponential backoff would allow the client to back off and retry, preventing quota exhaustion.
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.
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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