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PCDE Practice Question: A BI team runs a daily query on a BigQuery table…
A BI team runs a daily query on a BigQuery table 'events' partitioned by event_date. The query filters on event_date = CURRENT_DATE() and counts rows by event_type. The query is slow. Upon review, the table has 500 partitions but clustering is not set. Which action reduces query cost and latency?
⚠ Common exam trap
Google Cloud often tests the misconception that reducing data volume (e.g., by deleting old partitions or using wildcards) is the primary way to fix query performance, when in fact the correct solution is to optimize data access patterns within the existing partitions using clustering.
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
✓
Add clustering on event_type
Adding clustering on `event_type` physically co-locates rows with the same event type within each partition. This allows BigQuery to use block-level pruning when reading data, drastically reducing the number of bytes scanned for the COUNT(*) GROUP BY query. Since the query already filters on a single partition (`event_date = CURRENT_DATE()`), the performance bottleneck is scanning all rows in that partition; clustering eliminates that overhead without changing the table's structure or retention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recreate the table with only the last 30 days of data
Why it's wrong here
Deleting old data reduces total size but doesn't improve query efficiency for the current day.
- ✗
Use a wildcard table for daily ingestion
Why it's wrong here
Wildcard tables don't improve performance; they might complicate queries.
- ✗
Increase the partition expiration to 365 days
Why it's wrong here
Longer expiration increases storage cost, doesn't improve query speed.
- ✓
Add clustering on event_type
Why this is correct
Clustering on event_type organizes data by that column within each partition, speeding up count and group by.
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