Google ACE Planning and Configuring a Cloud Solution Practice Question

A company needs to store and analyze large amounts of log data (hundreds of terabytes) with occasional SQL queries. The data is rarely accessed after 30 days and must be kept for compliance for 7 years. They want to minimize storage costs. Which three actions should they take? (Choose THREE.)

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

✓

Use Cloud Storage Object Lifecycle Management to move objects to Nearline after 30 days, then to Coldline after 90 days, then to Archive after 1 year

BigQuery is ideal for log analysis. For historical data, moving older data to lower-cost storage classes like NEARLINE or COLDLINE reduces cost. Partitioning and clustering improve query performance and reduce costs. Cloud Storage is an alternative, but BigQuery is better for SQL queries.

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 Storage Object Lifecycle Management to move objects to Nearline after 30 days, then to Coldline after 90 days, then to Archive after 1 year
Why this is correct
Cloud Storage Object Lifecycle Management lets you define age-based rules that automatically transition objects from Standard to Nearline after 30 days, to Coldline after 90 days, and to Archive after 365 days. This reduces storage costs progressively while still making the logs retrievable for the required 7-year compliance window. Because objects remain in the bucket throughout, no data is deleted, and Archive class is specifically designed for long-term retention with the lowest per-GB cost.
✓
Store the data in BigQuery and use clustering on frequently filtered columns
Why this is correct
Clustering in BigQuery physically reorders data based on the values of designated columns, such as user_id or timestamp, that are frequently used in filter clauses. When queries filter on those clustered columns, BigQuery uses block-level pruning to scan only relevant clusters, dramatically reducing the number of bytes billed and improving query performance. Clustering is free to enable and does not require partitioning; it is especially effective for high-cardinality columns where queries return a subset of rows, though it does not reduce per-byte storage costs.
✓
Export older partitions from BigQuery to Cloud Storage and delete them from BigQuery after 30 days
Why this is correct
BigQuery storage pricing is lower for data not modified in the last 90 days, but exporting older partitions to Cloud Storage and deleting them from BigQuery can cut costs further by moving data to Nearline or Archive class. This pattern preserves the logs for the 7-year compliance requirement while freeing up BigQuery storage, and you can later query the exported data via external tables or re-import it if needed. The trade-off is that querying external data incurs query costs and may be slower than querying native BigQuery tables.
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Store the data in BigQuery and set an expiration on the table to delete data after 30 days
Why it's wrong here
Setting a table expiration in BigQuery causes the entire table to be automatically deleted after 30 days, which would destroy all log data and violate the 7-year retention requirement. Table expiration is a blunt mechanism intended for ephemeral or temporary datasets, not for tiering historical data. It cannot be scoped to only old partitions and provides no cost benefit for retention — it simply purges data permanently.
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Use BigQuery partitions based on ingestion time and set partition expiration to 30 days
Why it's wrong here
BigQuery partition expiration based on ingestion time automatically deletes entire partitions once the partition reaches the expiration age, so all records older than 30 days are removed. This approach efficiently manages time-bounded, ephemeral datasets, but it does not meet the 7-year compliance requirement because the logs are permanently deleted. Partition expiration does not offer any archival or lower-cost storage tier; it is a data deletion mechanism, not a lifecycle transition.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

Google ACE Planning and Configuring a Cloud Solution Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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