The correct choice is to use a data versioning tool like DVC to track datasets and ensure the pipeline always uses the correct version. This prevents data version mismatch, which is the root cause of the failed deployment—when the data processing step changed, the pipeline silently used a different dataset version, producing an invalid model. Data versioning tools create hash-based pointers stored in Git, so the CI/CD pipeline retrieves the exact dataset used during training, guaranteeing reproducibility even when data schemas or content evolve. On the Google Professional Machine Learning Engineer exam, this tests your understanding of ML reproducibility in CI/CD pipelines, a common scenario where candidates mistakenly focus on code or environment fixes instead of data lineage. A frequent trap is assuming library pinning or manual reviews catch data drift, but only versioning ensures deterministic dataset retrieval. Memory tip: think “data, not just code, needs a commit hash” to recall that data versioning is the key to reproducible ML pipelines.
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 team is building a CI/CD pipeline for ML using Cloud Build. The pipeline trains a model and deploys it to Vertex AI. Recently, a change in the data processing step caused the model to be trained with a different data version, leading to a failed deployment because the model was invalid. How should the team prevent this in the future?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a data versioning tool (e.g., DVC) to track datasets and ensure the pipeline always uses the correct version
Option C is correct because the root cause is a data version mismatch, not a code or environment issue. A data versioning tool like DVC (Data Version Control) tracks dataset versions via hash-based pointers in Git, ensuring the pipeline retrieves the exact dataset version used during training. This prevents silent failures when data processing steps change the data schema or content, which library pinning or manual reviews cannot guarantee.
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.
✗
Add a manual review step before training
Why it's wrong here
Manual reviews do not guarantee reproducibility and are not scalable.
✗
Pin all library versions in the Docker image
Why it's wrong here
Library versions are important but do not control data versioning.
✓
Use a data versioning tool (e.g., DVC) to track datasets and ensure the pipeline always uses the correct version
Why this is correct
Data versioning ensures reproducibility and consistency across pipeline runs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Schedule a cron job to check for data changes
Why it's wrong here
A cron job does not enforce that the training step uses the correct version.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse environment reproducibility (pinning libraries) with data reproducibility, assuming that locking code dependencies is sufficient to prevent model failures caused by data drift or version changes.
Detailed technical explanation
How to think about this question
DVC works by storing a hash of the dataset (e.g., MD5 or SHA256) in a .dvc file committed to Git, and the actual data is stored in a remote cache (e.g., S3, GCS). When the pipeline runs, it checks out the exact dataset version referenced by that hash, ensuring reproducibility even if the data processing step changes. In a real-world scenario, if a data engineer modifies a preprocessing script that inadvertently drops columns, DVC would detect the change as a new dataset version, and the pipeline would fail at the data retrieval step rather than silently training a broken model.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 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: Use a data versioning tool (e.g., DVC) to track datasets and ensure the pipeline always uses the correct version — Option C is correct because the root cause is a data version mismatch, not a code or environment issue. A data versioning tool like DVC (Data Version Control) tracks dataset versions via hash-based pointers in Git, ensuring the pipeline retrieves the exact dataset version used during training. This prevents silent failures when data processing steps change the data schema or content, which library pinning or manual reviews cannot guarantee.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data science team is using a shared Cloud Storage bucket to store training data. Multiple team members are simultaneously uploading new data files, and occasionally the wrong version of a file is used in training, leading to inconsistent results. Which best practice should the team implement to ensure data version consistency?
easy
A.Use Cloud Composer to schedule a daily snapshot of the Cloud Storage bucket.
B.Migrate all training data to BigQuery and use time-travel queries to access historical versions.
✓ C.Enable object versioning on the Cloud Storage bucket and use the version ID when referencing data files.
D.Restrict write access to the bucket to only one team member using IAM roles.
Why C: Option C is correct because enabling object versioning on a Cloud Storage bucket preserves each object's history, allowing the team to reference a specific version ID when reading data files. This ensures that every training run uses the exact same version of a file, eliminating inconsistency from concurrent uploads. The version ID acts as an immutable pointer, decoupling the training process from the bucket's live state.
Last reviewed: Jun 24, 2026
Question Discussion
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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.
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