- A
Do not partition; only cluster by `sale_date`.
Why wrong: Without partitioning, queries cannot prune entire storage blocks based on date, leading to scanning more data.
- B
Partition by `sale_date` and set a table expiration of 90 days.
Why wrong: Table expiration deletes data automatically but doesn't improve query performance for existing data.
- C
Partition the table by `sale_date` and cluster by `product_id`.
Partitioning by date enables partition elimination on date filters; clustering by product_id co-locates rows with the same product_id within each partition, improving GROUP BY performance.
- D
Partition by `product_id` and cluster by `sale_date`.
Why wrong: product_id has high cardinality (many distinct values), making partitioning inefficient (many small partitions) and clustering on date does not help date-range queries as much.
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 team runs regular analytical queries on a BigQuery table that stores 2 years of sales data (approximately 10 TB). Queries frequently filter on a `sale_date` column and also group by `product_id`. To optimize cost and performance, which design approach is most effective?
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 `sale_date` and cluster by `product_id`.
Option C is correct because partitioning by `sale_date` allows BigQuery to perform partition pruning, eliminating scans of irrelevant date ranges, while clustering by `product_id` physically co-locates rows with the same product ID within each partition. This combination minimizes the data scanned for queries that filter on `sale_date` and group by `product_id`, directly reducing both cost (bytes billed) and query latency.
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.
- ✗
Do not partition; only cluster by `sale_date`.
Why it's wrong here
Without partitioning, queries cannot prune entire storage blocks based on date, leading to scanning more data.
- ✗
Partition by `sale_date` and set a table expiration of 90 days.
Why it's wrong here
Table expiration deletes data automatically but doesn't improve query performance for existing data.
- ✓
Partition the table by `sale_date` and cluster by `product_id`.
Why this is correct
Partitioning by date enables partition elimination on date filters; clustering by product_id co-locates rows with the same product_id within each partition, improving GROUP BY performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Partition by `product_id` and cluster by `sale_date`.
Why it's wrong here
product_id has high cardinality (many distinct values), making partitioning inefficient (many small partitions) and clustering on date does not help date-range queries as much.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that partitioning by a high-cardinality column like `product_id` is acceptable, but the trap here is that BigQuery enforces a hard limit of 4,000 partitions per table, making such a design infeasible and forcing candidates to recognize that clustering is the correct mechanism for high-cardinality grouping columns.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery stores each partition as a separate block of storage, and clustering sorts data within each partition based on the clustering column(s). When a query groups by `product_id`, BigQuery can leverage the clustered order to perform efficient aggregation without scanning all rows in the partition. In practice, for a 10 TB table with 2 years of daily data, partitioning by day yields ~730 partitions, well within limits, and clustering by `product_id` (even with millions of distinct IDs) significantly reduces the bytes processed for GROUP BY queries.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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 `sale_date` and cluster by `product_id`. — Option C is correct because partitioning by `sale_date` allows BigQuery to perform partition pruning, eliminating scans of irrelevant date ranges, while clustering by `product_id` physically co-locates rows with the same product ID within each partition. This combination minimizes the data scanned for queries that filter on `sale_date` and group by `product_id`, directly reducing both cost (bytes billed) and query latency.
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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Last reviewed: Jun 30, 2026
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