PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?
⚠ Common exam trap
The trap is confusing Git LFS with DVC — both handle large files, but only DVC provides dataset versioning, pipeline reproducibility, and remote storage abstraction for ML workflows.
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 to integrate with Git and version large datasets and ML models by storing metadata and pointers in Git while keeping the actual data in remote storage. It provides commands like dvc add, dvc push, and dvc pull that mirror Git workflows, making it the right choice for versioning datasets alongside code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BigQuery table snapshots
Why it's wrong here
BigQuery table snapshots preserve table state at a point in time for query and restore, but they are not Git-integrated and cannot track arbitrary large dataset files in a repository. They tempt for data versioning generally, yet the stem's Git integration requirement is unmet.
- ✗
Delta Lake
Why it's wrong here
Delta Lake provides ACID transactions and time travel over data lakes, but it does not integrate with Git to track large files; its history lives in its own transaction log. It tempts where table-level versioning is wanted, yet the stem requires Git-coupled file tracking, which Delta Lake does not perform.
- ✗
Git LFS
Why it's wrong here
Git LFS handles large files but lacks data pipeline and experiment tracking features.
- ✓
DVC
Why this is correct
DVC stores large dataset files outside Git while committing small metafiles that capture content hashes and versions, so Git tracks dataset changes without bloating the repository. This pointer-based mechanism satisfies the requirement to version data alongside code using Git.
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Same concept, more angles
1 more way this is tested on PMLE
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. 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?
medium- A.Cloud Storage Object Versioning
- B.BigQuery table snapshots
- C.Git LFS
- ✓ D.DVC
Why D: 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.
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.