The answer is partitioning by day on the timestamp column and clustering on customer_id. Partitioning by day allows BigQuery to perform partition pruning, meaning that when a query filters by a specific time range, only the relevant daily partitions are scanned instead of the entire table, which directly reduces data read and lowers cost. Clustering on customer_id then sorts the data within each partition by that key, enabling BigQuery to use block-level pruning for queries filtering on customer_id, further improving query performance for time-series data. On the Google Professional Data Engineer exam, this tests your understanding of how to balance performance and cost in BigQuery table design—a common trap is to over-partition (e.g., by hour) which creates too many small partitions and increases metadata overhead. Remember the memory tip: “Partition to prune the past, cluster to find the customer fast.”
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
Cluster the table on customer_id
Why this is correct
Improves performance for queries filtering on customer_id.
Related concept
Read the scenario before looking for a memorised answer.
Denormalization can help but not directly for these filters.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
BigQuery partitioning by day creates separate storage blocks (partitions) for each day's data, and clustering sorts data within partitions by customer_id, enabling block-level pruning during queries. Under the hood, clustering uses a sort-based approach that organizes data into blocks of approximately 1 GB, and automatic reclustering periodically rewrites blocks to maintain sort order without user intervention or extra cost. In a real-world scenario, a query filtering on a 30-day range and a specific customer_id would scan only the relevant daily partitions and then only the blocks containing that customer_id, drastically reducing bytes processed compared to a full table scan.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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