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 analyst frequently queries a BigQuery table that contains an array of structs representing product purchases. The query below runs slowly:
SELECT customer_id, COUNT(purchase) as total_purchases
FROM sales, UNNEST(purchases) as purchase
GROUP BY customer_id
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a materialized view that pre-aggregates by customer_id and purchase count
The query runs slowly because it must unnest the `purchases` array for every row and then aggregate. A materialized view pre-aggregates the data by `customer_id` and purchase count, avoiding repeated full scans and unnesting. This is the most impactful optimization because it eliminates the compute cost of UNNEST and GROUP BY at query time.
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.
✓
Create a materialized view that pre-aggregates by customer_id and purchase count
Why this is correct
A materialized view pre-computes the aggregation, so queries read the view instead of scanning the full table.
Related concept
Read the scenario before looking for a memorised answer.
✗
Partition the table by transaction date
Why it's wrong here
Partitioning by date speeds up date-based filters but does not help with aggregation on nested data without a filter.
✗
Use a subquery to filter purchases first
Why it's wrong here
A subquery does not reduce the amount of data scanned significantly; it still requires reading the entire table.
✗
Cluster the table by purchases.product_id
Why it's wrong here
Clustering on a nested field may not be effective because UNNEST flattens the array, reducing clustering benefits.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any indexing or partitioning strategy (like clustering or partitioning) universally speeds up all queries, when in fact the fix must target the specific expensive operation — here, the UNNEST and GROUP BY — rather than adding a generic optimization.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery materialized views are automatically refreshed and can be queried directly or used by the optimizer to rewrite queries. The UNNEST operation is a lateral cross join that explodes each row's array into multiple rows, which is CPU- and I/O-intensive; a materialized view stores the pre-joined, pre-aggregated result, so the query engine reads far fewer bytes. In a real-world scenario, a sales table with millions of customers and hundreds of purchases per customer would see query times drop from minutes to seconds.
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: Create a materialized view that pre-aggregates by customer_id and purchase count — The query runs slowly because it must unnest the `purchases` array for every row and then aggregate. A materialized view pre-aggregates the data by `customer_id` and purchase count, avoiding repeated full scans and unnesting. This is the most impactful optimization because it eliminates the compute cost of UNNEST and GROUP BY at query time.
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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