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 uses BigQuery to generate daily sales reports. The query aggregates sales by product category and region. The table 'sales_raw' is 500 GB and is updated every hour with new transactions. The report runs slowly. What is the most cost-effective method to improve query performance without changing the existing table schema?
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 aggregates sales by product category and region
Option C is correct because a materialized view in BigQuery pre-computes and stores the aggregated results of the query, allowing subsequent queries to read the pre-aggregated data instead of scanning the entire 500 GB 'sales_raw' table. This reduces both the data scanned and the query execution time, and it is automatically refreshed when the base table is updated (every hour), making it cost-effective as you only pay for the bytes used by the materialized view and the incremental refreshes, not for full table scans.
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 product category
Why it's wrong here
Partitioning by category could lead to many partitions, and may not improve performance for cross-region queries.
✗
Create a separate summary table using scheduled queries
Why it's wrong here
Manual summary tables add overhead and require maintenance; not as cost-effective as materialized views.
✓
Create a materialized view that aggregates sales by product category and region
Read the scenario before looking for a memorised answer.
✗
Cluster the table by region
Why it's wrong here
Clustering alone may not reduce scan size enough if the query aggregates across many categories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between partitioning/clustering (which optimize data scanning but do not pre-compute results) and materialized views (which store pre-computed results), leading candidates to choose partitioning or clustering as a 'quick fix' without realizing they do not eliminate the need for full aggregation scans.
Detailed technical explanation
How to think about this question
BigQuery materialized views use a combination of incremental refresh and base table change tracking to keep the pre-aggregated results up-to-date without reprocessing the entire table. Under the hood, BigQuery stores the materialized view as a separate table with its own storage and metadata, and when the base table is modified, only the changed partitions or rows are re-aggregated. This is especially efficient for high-frequency updates (every hour) because the incremental refresh cost is proportional to the volume of changes, not the full table size. In real-world scenarios, this can reduce query costs by 90% or more compared to scanning the raw table for each report.
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.
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 that aggregates sales by product category and region — Option C is correct because a materialized view in BigQuery pre-computes and stores the aggregated results of the query, allowing subsequent queries to read the pre-aggregated data instead of scanning the entire 500 GB 'sales_raw' table. This reduces both the data scanned and the query execution time, and it is automatically refreshed when the base table is updated (every hour), making it cost-effective as you only pay for the bytes used by the materialized view and the incremental refreshes, not for full table scans.
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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