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PDE Practice Question: A data engineer is designing a BigQuery table for…
A data engineer is designing a BigQuery table for time-series data that will be queried frequently by time range and also by a customer_id. Which TWO design decisions will improve query performance and manage costs? (Choose two.)
⚠ Common exam trap
Google Cloud often tests the misconception that disabling automatic reclustering saves costs, but in reality it is free and essential for maintaining clustering benefits, while partition expiration is a lifecycle management feature, not a performance optimization.
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 the table by day on the timestamp column
Partitioning the table by day on the timestamp column allows BigQuery to prune partitions when queries filter by a time range, scanning only the relevant partitions instead of the entire table. This directly reduces the amount of data read, improving query performance and lowering costs.
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 the table by day on the timestamp column
Why this is correct
Enables partition pruning for time-range queries.
- ✓
Cluster the table on customer_id
Why this is correct
Improves performance for queries filtering on customer_id.
- ✗
Disable automatic reclustering to save costs
Why it's wrong here
Automatic reclustering helps maintain performance.
- ✗
Set partition expiration to 1 year
Why it's wrong here
Manages data retention but not performance.
- ✗
Use nested repeated fields for customer data
Why it's wrong here
Denormalization can help but not directly for these filters.
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