easyMultiple Choice
PMLE Practice Question: A data science team is using a shared Cloud…
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?
⚠ Common exam trap
Google Cloud often tests the distinction between data versioning (object-level immutability) and data backup (snapshots or time-travel), leading candidates to choose snapshot or database-centric solutions that do not provide per-file version consistency in a shared object store.
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
✓
Enable object versioning on the Cloud Storage bucket and use the version ID when referencing data files.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Composer to schedule a daily snapshot of the Cloud Storage bucket.
Why it's wrong here
Scheduled snapshots copy bucket contents at fixed intervals, so concurrent uploads between snapshots still yield mixed versions during training. Snapshots are tempting for point-in-time recovery, but consistency requires object versioning or immutable generation-based references that pin each training run to exact object versions.
- ✗
Migrate all training data to BigQuery and use time-travel queries to access historical versions.
Why it's wrong here
Time-travel queries read historical rows within BigQuery's retention window; they do not version objects in a Cloud Storage bucket, and migrating training data changes the storage platform rather than adding version control. Time travel suits auditing or recovering recently changed warehouse tables, not ensuring consistent object versions.
- ✓
Enable object versioning on the Cloud Storage bucket and use the version ID when referencing data files.
Why this is correct
Object versioning retains every overwrite as a distinct, immutable generation, so concurrent uploads no longer silently replace earlier files. Referencing the explicit version ID pins each training run to one exact object, satisfying the requirement for consistent, reproducible data.
- ✗
Restrict write access to the bucket to only one team member using IAM roles.
Why it's wrong here
Restricting writes to one person serialises uploads but provides no versioning mechanism, so a file can still be overwritten and the prior object lost. IAM write restriction suits least-privilege access control, not consistency. Object Versioning retains each upload as a distinct generation, letting training reference a specific version.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.