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.
Exhibit
Refer to the exhibit.
Table details: orders (10 TB, daily partitioned by order_date, no clustering).
Query:
SELECT customer_id, COUNT(item) as items_purchased
FROM orders, UNNEST(items) as item
WHERE item.category = 'electronics'
GROUP BY customer_id
ORDER BY items_purchased DESC
The query above runs slowly on the 10 TB table. Which optimization would most improve performance?
Refer to the exhibit.
Table details: orders (10 TB, daily partitioned by order_date, no clustering).
Query:
SELECT customer_id, COUNT(item) as items_purchased
FROM orders, UNNEST(items) as item
WHERE item.category = 'electronics'
GROUP BY customer_id
ORDER BY items_purchased DESC
A
Use a subquery to filter item.category first
Why wrong: A subquery does not physically reorganize data; the database still scans the same amount of data.
B
Cluster the table by customer_id
Why wrong: Clustering by customer_id helps the GROUP BY but does not reduce the full table scan because the WHERE clause filters on a nested field, which still requires scanning all partitions.
C
Create a materialized view that pre-aggregates by customer_id and item category
A materialized view pre-computes the COUNT for each (customer_id, category), so the query reads a small pre-aggregated table.
D
Partition the table by order_date
Why wrong: The table is already partitioned by order_date, but the query does not filter on a date range, so no partition pruning occurs.
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 item category
Option C is correct because a materialized view can pre-compute and store the aggregated results by customer_id and item category, eliminating the need to scan the full 10 TB table for each query. This dramatically reduces I/O and computation time, especially when the underlying aggregation is expensive and the query pattern is predictable.
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.
✗
Use a subquery to filter item.category first
Why it's wrong here
A subquery does not physically reorganize data; the database still scans the same amount of data.
✗
Cluster the table by customer_id
Why it's wrong here
Clustering by customer_id helps the GROUP BY but does not reduce the full table scan because the WHERE clause filters on a nested field, which still requires scanning all partitions.
✓
Create a materialized view that pre-aggregates by customer_id and item category
Why this is correct
A materialized view pre-computes the COUNT for each (customer_id, category), so the query reads a small pre-aggregated table.
Related concept
Read the scenario before looking for a memorised answer.
✗
Partition the table by order_date
Why it's wrong here
The table is already partitioned by order_date, but the query does not filter on a date range, so no partition pruning occurs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that partitioning or clustering alone can accelerate arbitrary aggregation queries, when in fact they only help with filter-based pruning or specific join patterns, not with reducing the full scan required for grouping without a WHERE clause.
Detailed technical explanation
How to think about this question
Materialized views in databases like PostgreSQL or Oracle store the result set of a query as a physical table, which can be refreshed on demand or automatically. Under the hood, the database can use index-only scans on the materialized view if indexes are created on the grouped columns, and the view can be significantly smaller than the base table (e.g., reducing 10 TB to a few GB). In real-world scenarios, this is critical for dashboards that repeatedly run the same aggregation on massive fact tables, where even incremental refresh strategies can keep the view nearly up-to-date without reprocessing the entire dataset.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 item category — Option C is correct because a materialized view can pre-compute and store the aggregated results by customer_id and item category, eliminating the need to scan the full 10 TB table for each query. This dramatically reduces I/O and computation time, especially when the underlying aggregation is expensive and the query pattern is predictable.
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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