The answer is to create a custom service account with the required permissions and assign it to the Dataflow job via the `--serviceAccount` option. This is the most efficient solution because it directly grants the Dataflow workers the specific roles they need—such as `roles/storage.objectViewer` for reading Cloud Storage data—without over-provisioning access to other pipeline components like Cloud Composer or Vertex AI. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the principle of least privilege and service account delegation in data pipelines. A common trap is to modify the Cloud Composer environment’s default service account or make the storage bucket public, both of which introduce security risks or unnecessary complexity. Instead, remember that Dataflow jobs can accept their own identity, isolating permissions to the worker level. Memory tip: “Dataflow gets its own ID” — always assign a dedicated service account to the job, not the environment.
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 a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a custom service account with required permissions and assign it to the Dataflow job.
The most efficient way to resolve insufficient permissions for Dataflow workers accessing Cloud Storage is to create a custom service account with the required roles (e.g., roles/storage.objectViewer) and assign it to the Dataflow job via the --serviceAccount option. This follows the principle of least privilege and ensures that only the Dataflow workers have the necessary permissions, without affecting other pipeline components or exposing the bucket publicly.
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.
✓
Create a custom service account with required permissions and assign it to the Dataflow job.
Why this is correct
Lets the Dataflow worker access the data securely.
Related concept
Read the scenario before looking for a memorised answer.
✗
Grant the 'roles/storage.objectViewer' role to 'allUsers' on the Cloud Storage bucket.
Why it's wrong here
This opens the bucket to the public, a security risk.
✗
Use the Composer environment's service account for all pipeline components.
Why it's wrong here
Composer service account may lack data access or have overly broad permissions.
✗
Move the Dataflow job to run after the pipeline so that data is already processed.
Why it's wrong here
Does not address the permission issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that using a single service account for all components (like the Composer environment's service account) is simpler and sufficient, but this ignores the principle of least privilege and can cause security vulnerabilities or permission conflicts in distributed pipelines.
Detailed technical explanation
How to think about this question
Dataflow workers use a service account to authenticate with Google Cloud services; by default, they use the Compute Engine default service account, which may lack specific permissions. Assigning a custom service account with fine-grained roles (e.g., roles/storage.objectViewer for reading data, roles/dataflow.worker for running the job) ensures that workers have exactly the permissions needed. In a real-world scenario, you might also need to grant the service account access to Vertex AI or other services if the pipeline interacts with them, but the key is to avoid using overly permissive accounts like the Composer environment's service account, which could lead to privilege escalation.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Create a custom service account with required permissions and assign it to the Dataflow job. — The most efficient way to resolve insufficient permissions for Dataflow workers accessing Cloud Storage is to create a custom service account with the required roles (e.g., roles/storage.objectViewer) and assign it to the Dataflow job via the --serviceAccount option. This follows the principle of least privilege and ensures that only the Dataflow workers have the necessary permissions, without affecting other pipeline components or exposing the bucket publicly.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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