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PDE Practice Question: An online retailer uses BigQuery for analytics
An online retailer uses BigQuery for analytics. They have a time-series table with 5 billion rows and new data arrives every day. They want to optimize query performance and reduce costs by ensuring that queries scan only the partitions they need. Which table design should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between partitioning (which prunes data at the storage level) and clustering (which only sorts data within a partition or table), leading candidates to mistakenly believe clustering alone can reduce bytes scanned for time-range queries.
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 a table partitioned on the timestamp column.
Partitioning on the timestamp column allows BigQuery to perform partition pruning, so queries with filters on that column only scan the relevant partitions. This directly reduces the amount of data read, lowering both query cost (pay-per-byte) and improving performance. For a 5-billion-row table with daily data arrival, time-unit partitioning is the standard design to meet the stated goals.
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 a table partitioned on the timestamp column.
Why this is correct
Allows queries to scan only relevant time-range partitions.
- ✗
Use a table clustered on the timestamp column.
Why it's wrong here
Clustering organizes data within partitions but does not reduce scanned partitions.
- ✗
Use a table with no partitioning but use LIMIT in queries.
Why it's wrong here
LIMIT reduces rows returned but not scanned bytes.
- ✗
Use a table partitioned by ingestion time with a partition expiration.
Why it's wrong here
Effective only if data is queried by ingestion time, not the actual timestamp.
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