The correct answer is to add the labeler as a participant in the labeling task and assign IAM roles on the dataset. This works because the AI Platform Data Labeling Service uses dataset-level IAM permissions to control access for labelers, not project-level roles or Cloud Storage ACLs. By granting the Data Labeling Service role directly on the dataset resource, you provide the precise access needed for the labeling task without exposing the entire project. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of granular access control versus broad permissions—a common trap is assuming you need to share the whole project or use storage bucket permissions. Remember that labeling tasks are collaborative and isolated to the dataset, so think “dataset role, not project role.” A useful memory tip: “Label the dataset, not the project.”
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. 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 team is using AI Platform Data Labeling Service to label data for a classification model. They want to allow a labeler from a different team to work on the same dataset. What is the correct way to grant access?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add the labeler as a participant in the labeling task and assign IAM roles on the dataset
Option D is correct because labeling tasks are shared by granting the labeler role on the dataset resource. Option A is wrong because sharing the entire project gives too much access. Option B is wrong because the Data Labeling Service does not use Cloud Storage ACLs for task access. Option C is wrong because exporting and reimporting causes duplication.
Key principle: ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Add the labeler's account as a Project Editor on the project
Share the Cloud Storage bucket containing the data with the labeler
Why it's wrong here
Bucket access does not grant access to labeling tasks.
✗
Export the dataset and have the labeler create a new dataset
Why it's wrong here
This creates duplicate work and does not allow collaboration on the same task.
✓
Add the labeler as a participant in the labeling task and assign IAM roles on the dataset
Why this is correct
The Data Labeling Service allows adding participants to tasks, and IAM roles control access.
Related concept
Standard ACLs match source addresses.
Common exam traps
Common exam trap: ACLs stop at the first match
ACLs are processed top to bottom. The first matching entry wins, and an implicit deny usually exists at the end.
Detailed technical explanation
How to think about this question
ACL questions test precision: source, destination, protocol, port and direction. A generally correct ACL can still fail if it is applied on the wrong interface or in the wrong direction.
KKey Concepts to Remember
Standard ACLs match source addresses.
Extended ACLs can match source, destination, protocol and ports.
The first matching ACL entry is used.
There is usually an implicit deny at the end.
TExam Day Tips
→Check inbound versus outbound direction.
→Read the ACL from top to bottom.
→Look for a broader permit or deny above the intended line.
Key takeaway
ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
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.
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related PMLE ACL questions on filtering logic and placement.
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Standard ACLs match source addresses..
What is the correct answer to this question?
The correct answer is: Add the labeler as a participant in the labeling task and assign IAM roles on the dataset — Option D is correct because labeling tasks are shared by granting the labeler role on the dataset resource. Option A is wrong because sharing the entire project gives too much access. Option B is wrong because the Data Labeling Service does not use Cloud Storage ACLs for task access. Option C is wrong because exporting and reimporting causes duplication.
What should I do if I get this PMLE question wrong?
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related PMLE ACL questions on filtering logic and placement.
What is the key concept behind this question?
Standard ACLs match source addresses.
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Question Discussion
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