PCDE Practice Question: Define data structures and implement SQL for Business Intelligence
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. 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 company runs a retail BI dashboard on BigQuery. The fact_sales table is partitioned by DAY and clustered by product_id. The table is 10 TB. Recently, analysts complain that queries filtering on a specific product_id and a month of data take over 10 minutes. The query uses a subquery to find top products. What should the engineer do?
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 for the subquery.
Option A is correct because creating a materialized view for the subquery that identifies top products pre-computes and stores the results, which are incrementally refreshed by BigQuery. This avoids re-scanning the entire 10 TB fact_sales table each time the query runs, drastically reducing query time for the analysts' frequent filtering on product_id and a month of data.
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 for the subquery.
Why this is correct
Materialized view stores precomputed results, reducing query time and cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add an ORDER BY product_id to the subquery.
Why it's wrong here
ORDER BY does not reduce data scanned; it adds sorting overhead.
✗
Change partition type to HOUR.
Why it's wrong here
Hourly partitions may improve day-level filters but do not eliminate the need to scan all partitions for a month.
✗
Re-cluster the table with product_id as the first clustering column and date as the second.
Why it's wrong here
Current clustering is already on product_id; re-ordering columns does not change performance significantly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that clustering or partitioning changes alone can solve performance issues for subqueries, but the real bottleneck is the repeated full-table scan, which only a materialized view or similar pre-computation can eliminate.
Detailed technical explanation
How to think about this question
BigQuery materialized views are automatically and incrementally refreshed using the base table's change history, so they stay consistent without manual maintenance. Under the hood, the materialized view stores pre-joined and pre-aggregated results in a separate, optimized storage layer, allowing queries that reference it to bypass scanning the full 10 TB table. In a real-world scenario, a retail dashboard with frequent top-product filters would see query times drop from minutes to seconds because the materialized view serves as a pre-computed summary.
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.
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — 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 for the subquery. — Option A is correct because creating a materialized view for the subquery that identifies top products pre-computes and stores the results, which are incrementally refreshed by BigQuery. This avoids re-scanning the entire 10 TB fact_sales table each time the query runs, drastically reducing query time for the analysts' frequent filtering on product_id and a month of data.
What should I do if I get this PCDE 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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