- A
Denormalized table with repeated fields
Why wrong: Repeated fields (arrays) are for one-to-many relationships, not for typical fact/dimension drill-down.
- B
Single wide table with all dimensions and measures
Why wrong: Wide tables increase storage and scan costs; updates are expensive and not optimal for drill-down.
- C
Star schema with fact table and dimension tables
Star schema is optimized for BI: fact table stores measures, dimensions store attributes, enabling flexible aggregation and drill-down.
- D
Snowflake schema with normalized dimensions
Why wrong: Snowflake adds extra joins, increasing complexity and query cost without performance benefit in BigQuery.
Question 229 of 1,000
Define data structures and implement SQL for Business Intelligence →easyMultiple ChoiceObjective-mapped
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 BI analyst wants to create a report that displays total revenue by product category and month, with ability to drill down to individual products. Which schema design supports this in BigQuery?
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
✓
Star schema with fact table and dimension tables
Option C is correct because a star schema with a central fact table (containing revenue measures) and separate dimension tables (for product category, month, and product) is the optimal design for BI reporting in BigQuery. This schema enables efficient aggregation by product category and month, while supporting drill-down to individual products via joins on the product dimension key. BigQuery's columnar storage and query engine are optimized for star schema joins, making this both performant and cost-effective.
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.
- ✗
Denormalized table with repeated fields
Why it's wrong here
Repeated fields (arrays) are for one-to-many relationships, not for typical fact/dimension drill-down.
- ✗
Single wide table with all dimensions and measures
Why it's wrong here
Wide tables increase storage and scan costs; updates are expensive and not optimal for drill-down.
- ✓
Star schema with fact table and dimension tables
Why this is correct
Star schema is optimized for BI: fact table stores measures, dimensions store attributes, enabling flexible aggregation and drill-down.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Snowflake schema with normalized dimensions
Why it's wrong here
Snowflake adds extra joins, increasing complexity and query cost without performance benefit in BigQuery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that denormalized or wide tables (Options A or B) are always faster for BI queries. However, in BigQuery, star schemas with proper clustering and partitioning outperform wide tables due to reduced I/O and better use of columnar pruning. The drill-down requirement from category to product is naturally supported by star schema joins.
Detailed technical explanation
How to think about this question
In BigQuery, star schemas leverage clustering on dimension keys (e.g., product_id, month) to minimize data scanned during joins, and the fact table can be partitioned by month for time-based pruning. Under the hood, BigQuery's distributed query engine uses broadcast joins for small dimension tables, making star schema queries highly efficient even at petabyte scale. A real-world scenario is a retail dashboard where the analyst needs to aggregate revenue by category (high-level) and then drill into product-level details without rewriting queries—star schema supports this via hierarchical dimension keys.
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
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FAQ
Questions learners often ask
What does this PCDE question test?
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: Star schema with fact table and dimension tables — Option C is correct because a star schema with a central fact table (containing revenue measures) and separate dimension tables (for product category, month, and product) is the optimal design for BI reporting in BigQuery. This schema enables efficient aggregation by product category and month, while supporting drill-down to individual products via joins on the product dimension key. BigQuery's columnar storage and query engine are optimized for star schema joins, making this both performant and cost-effective.
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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Last reviewed: Jul 4, 2026
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