PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?
⚠ Common exam trap
The trap is assuming native GCS Object Versioning is sufficient for ML data versioning — candidates overlook that object-level versioning lacks dataset-level snapshots, lineage, and Git-integrated reproducibility that DVC provides.
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
✓
DVC
DVC (Data Version Control) is purpose-built for versioning large datasets and ML models alongside Git. It stores small metadata files in Git while pushing the actual large data to remote storage like Google Cloud Storage, enabling teams to track dataset changes over time and reproduce exact previous data states via commits or tags.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Storage Object Versioning
Why it's wrong here
Object Versioning retains overwritten or deleted object generations, but it does not provide dataset-level lineage, commit history, or reproducible snapshots across many objects. It is tempting because it is native to Cloud Storage, yet ML data versioning needs tooling such as DVC or Vertex AI Datasets to reproduce prior states.
- ✗
BigQuery table snapshots
Why it's wrong here
BigQuery table snapshots capture BigQuery-managed table data at a point in time; they cannot version objects sitting in a Cloud Storage bucket. They are the right choice for reproducing historical states of BigQuery tables. Here the datasets live in GCS, so snapshots never see them.
- ✗
Git LFS
Why it's wrong here
Git LFS versions files by pointer within a Git repository, so it cannot track object-level changes across a GCS bucket's millions of objects. It suits versioning code and small binaries alongside source. The stem requires reproducing prior dataset states in Cloud Storage, which needs a purpose-built data versioning layer.
- ✓
DVC
Why this is correct
DVC versions large datasets held in Google Cloud Storage by storing content hashes in small metafiles committed to Git, leaving the data in place. This delivers change tracking and reproducible checkouts of previous data states, exactly the capability the team requires.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.