PDE Cloud Storage lifecycle management Practice Question
A company has a data lake on Cloud Storage with raw data in the 'raw' bucket, curated data in 'curated', and processed data in 'processed'. They want to implement lifecycle management to reduce costs. Which TWO actions should they take? (Choose 2)
⚠ Common exam trap
PDE often tests the misconception that enabling object versioning deletes old versions automatically, and the trap of choosing Archive too early without accounting for its 365-day minimum duration and retrieval costs.
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
✓
Set a lifecycle rule to change storage class from Standard to Nearline after 30 days for the 'raw' bucket.
Option A is correct because a lifecycle rule that transitions objects in the 'raw' bucket from Standard to Nearline after 30 days is a valid cost-reduction strategy for raw data that is accessed infrequently but may still be needed; Nearline is designed for data accessed less than once a month. Option D is correct because setting a lifecycle rule to delete objects older than 365 days in the 'curated' and 'processed' buckets removes stale data that is no longer needed, directly reducing storage costs. Option B is incorrect because enabling object versioning does not automatically delete older versions; versioning preserves them and typically increases storage costs unless combined with a separate lifecycle deletion rule. Option C is incorrect because partition expiration applies to BigQuery table partitions, not to objects in a Cloud Storage bucket, and the scenario is about Cloud Storage lifecycle management. Option E is incorrect because transitioning raw data to Archive after only 30 days is overly aggressive and would incur early-deletion charges and retrieval costs if the data is still needed, making it a poor fit compared with Nearline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set a lifecycle rule to change storage class from Standard to Nearline after 30 days for the 'raw' bucket.
Why this is correct
Raw data is typically accessed rarely after initial ingestion, so transitioning it from Standard to Nearline at 30 days cuts storage cost while preserving availability. This satisfies the stem's cost-reduction goal for the 'raw' bucket without affecting curated or processed data.
- ✗
Enable object versioning on all buckets to automatically delete older versions.
Why it's wrong here
Object versioning preserves noncurrent versions rather than deleting them, so it adds storage cost instead of reducing it. It is tempting because versioning protects against accidental overwrites, which suits compliance or recovery requirements, but lifecycle rules that delete or transition objects are what actually lower cost here.
- ✗
Set a partition expiration on BigQuery tables that reference data in the 'processed' bucket.
Why it's wrong here
Partition expiration deletes BigQuery table partitions, not the underlying Cloud Storage objects in the processed bucket, so storage cost remains. It is tempting because partition expiry genuinely reduces BigQuery storage and query cost, which fits cost control on warehouse tables rather than on data lake buckets.
- ✓
Set a lifecycle rule to delete objects older than 365 days in the 'curated' and 'processed' buckets.
Why this is correct
Curated and processed data usually retain value only for a bounded period, so an age-based deletion rule at 365 days removes stale objects and stops ongoing storage charges. This satisfies the stem's cost-reduction objective for those buckets while leaving raw data governed separately.
- ✗
Set a lifecycle rule to change storage class from Standard to Archive after 30 days for the 'raw' bucket.
Why it's wrong here
Setting a lifecycle rule to transition 'raw' data to Archive after 30 days is unsuitable because raw data in a data lake is typically either deleted once processed and moved to 'curated', or moved to a less expensive, but still accessible, cold storage tier like Coldline if long-term retention with infrequent access is needed. Archive storage incurs high retrieval costs and latency, making it impractical for data that might still require reprocessing or auditing within a relatively short timeframe. This action would be appropriate for data requiring very long-term retention (years) with extremely rare access, where retrieval costs are offset by minimal access frequency.
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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 PDE 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 PDE exam.