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PCDE A company uses BigQuery for BI reporting Practice Question
A company uses BigQuery for BI reporting. They have a table 'orders' with columns: order_id, customer_id, order_date, amount, status. The BI team frequently runs queries that filter on order_date and group by customer_id to compute total sales per customer. Which partitioning and clustering strategy optimizes query performance and cost?
⚠ Common exam trap
Google Cloud often tests the misconception that clustering alone is sufficient for performance, ignoring that partitioning is essential for date-range filters to avoid full table scans, or that clustering on a high-cardinality column like customer_id is ideal for GROUP BY but must be paired with a partition key that matches the filter pattern.
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
✓
Partition by order_date, cluster by customer_id
Partitioning by order_date allows BigQuery to prune partitions for queries filtering on order_date, reducing the amount of data scanned. Clustering by customer_id organizes data within each partition so that GROUP BY customer_id queries can efficiently read only relevant blocks, minimizing shuffle and cost. This combination directly aligns with the BI team's query pattern of filtering by date and aggregating by customer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition by order_date, cluster by status
Why it's wrong here
Clustering on status does not help the group by on customer_id.
- ✗
Do not partition, cluster by customer_id
Why it's wrong here
Without partitioning, all partitions are scanned even with date filters, increasing cost.
- ✗
Partition by customer_id, cluster by order_date
Why it's wrong here
Partitioning on customer_id is not effective because queries filter on order_date, not customer_id.
- ✓
Partition by order_date, cluster by customer_id
Why this is correct
Partitioning on order_date prunes partitions for date filters; clustering on customer_id improves group by performance.
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