The answer is that the custom service account lacks the 'bigquery.jobs.create' permission in the project. While the 'bigquery.dataViewer' role grants access to read table data, Vertex AI training jobs must first initiate a BigQuery job—specifically a query job—to pull the feature data, and this action requires the 'bigquery.jobs.create' permission at the project level. Without it, the job fails with an 'Access Denied' error despite having data-level read access. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the separation between data access and job execution permissions in BigQuery, a common trap where candidates assume 'dataViewer' alone is sufficient for programmatic reads. A useful memory tip is: "To read data, you need a job; to run a job, you need create."
PMLE Collaborating to manage data and models Practice Question
This PMLE practice question tests your understanding of collaborating 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 company uses BigQuery to store feature data for ML training. A data engineer notices that a Vertex AI Training job is failing with 'Access Denied' errors when reading from a BigQuery table. The training job uses a custom service account that has been granted the 'bigquery.dataViewer' role on the dataset. What is the most likely cause of the failure?
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 lacks the 'bigquery.jobs.create' permission in the project.
The 'bigquery.dataViewer' role grants permissions to read BigQuery data (e.g., bigquery.tables.getData), but it does not include the 'bigquery.jobs.create' permission. When a Vertex AI training job reads from BigQuery, it must first create a BigQuery job (a query job) to retrieve the data. Without 'bigquery.jobs.create' at the project level, the service account cannot initiate the read operation, resulting in an 'Access Denied' error even though it has data-level access.
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 service account is not in the same project as the BigQuery dataset.
Why it's wrong here
Cross-project access is possible with proper IAM; this is not the primary cause.
✗
The BigQuery table is partitioned and requires row-level access.
Why it's wrong here
Partitioning does not affect read permissions at the table level.
✓
The service account lacks the 'bigquery.jobs.create' permission in the project.
Why this is correct
Reading from BigQuery via Vertex AI Training requires the ability to submit a query job, which requires 'bigquery.jobs.create'.
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 training job does not have the required network access to BigQuery.
Why it's wrong here
Network access is usually configured separately; the error message indicates a permission issue, not network.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume 'bigquery.dataViewer' is sufficient for all read operations, overlooking the requirement for 'bigquery.jobs.create' to initiate the query job that actually reads the data.
Detailed technical explanation
How to think about this question
BigQuery uses a job-based architecture: any read operation (e.g., SELECT, table export) requires creating a job under a Google Cloud project, and the caller must have 'bigquery.jobs.create' on that project. This permission is distinct from data-level permissions like 'bigquery.tables.getData'. In Vertex AI, the training job's service account must have this permission on the project where the job runs (often the same project as the Vertex AI job), even if the data resides in a different project. A common real-world scenario is when a data scientist grants 'bigquery.dataViewer' on a dataset but forgets to grant 'bigquery.jobs.create' on the project, leading to confusing 'Access Denied' errors.
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.
Collaborating to manage data and models — This question tests Collaborating 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 service account lacks the 'bigquery.jobs.create' permission in the project. — The 'bigquery.dataViewer' role grants permissions to read BigQuery data (e.g., bigquery.tables.getData), but it does not include the 'bigquery.jobs.create' permission. When a Vertex AI training job reads from BigQuery, it must first create a BigQuery job (a query job) to retrieve the data. Without 'bigquery.jobs.create' at the project level, the service account cannot initiate the read operation, resulting in an 'Access Denied' error even though it has data-level access.
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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