hardMultiple SelectObjective-mapped
PCDE Practice Question: Reduce BigQuery query costs for their BI workloads
A company wants to reduce BigQuery query costs for their BI workloads. Which THREE actions effectively lower the amount of data processed per query? (Choose THREE.)
⚠ Common exam trap
Google Cloud often tests the misconception that row-limiting clauses like LIMIT reduce data processing costs, but in BigQuery, only column and partition pruning reduce the bytes scanned.
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 partitioned tables on date column
Partitioned tables in BigQuery allow queries to use the WHERE clause to filter on the partition column (e.g., a date column), so BigQuery can prune entire partitions from the scan. This directly reduces the amount of data read and billed, lowering query costs. Option A is correct because it is a primary cost-control mechanism in BigQuery.
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 partitioned tables on date column
Why this is correct
Partitioning limits query scans to relevant partitions, cutting bytes.
- ✗
Use LIMIT in subqueries to reduce output
Why it's wrong here
LIMIT does not reduce the scan of the underlying table; it only limits output rows.
- ✓
Use clustered tables on frequently filtered columns
Why this is correct
Clustering organizes data so that queries scan fewer blocks, reducing bytes.
- ✗
Use SELECT * to avoid missing columns
Why it's wrong here
SELECT * often scans all columns, increasing bytes.
- ✓
Use materialized views that match common query patterns
Why this is correct
Materialized views store pre-computed results, so queries read only the aggregated data.
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