The answer is that dimension tables in a star schema are intentionally denormalized to reduce joins for BI reporting. This is correct because denormalization consolidates related attributes into a single dimension table, allowing fact tables containing quantitative measures to join directly without traversing multiple normalized layers. In the Google Professional Cloud Database Engineer exam, this concept tests your understanding of OLAP design principles versus OLTP normalization, often appearing in scenario-based questions where you must choose between a star schema and a snowflake schema. A common trap is assuming that all schemas require normalized dimensions for data integrity, but for BI reporting, denormalization prioritizes query speed over storage efficiency. Remember the memory tip: “Star schemas flatten facts and dimensions; snowflakes normalize for storage intentions.”
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.
Which TWO statements are true about designing a star schema for BI reporting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Dimension tables are denormalized to reduce the number of joins
Option B is correct because dimension tables in a star schema are intentionally denormalized to reduce the number of joins required for BI queries. This denormalization improves query performance by allowing fact tables to join directly to dimension tables without traversing multiple normalized tables, which is a key design principle for OLAP reporting.
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.
✗
Fact tables store descriptive attributes like product names
Why it's wrong here
Descriptive attributes are stored in dimension tables, not fact tables.
✓
Dimension tables are denormalized to reduce the number of joins
Why this is correct
Denormalized dimensions allow joining directly to the fact table without additional joins.
Related concept
Read the scenario before looking for a memorised answer.
✗
Fact tables use natural keys to enforce referential integrity
Why it's wrong here
Fact tables typically use surrogate keys for better performance and handling of changes.
✓
Fact tables contain quantitative measures
Why this is correct
Measures are additive values stored in the fact table.
Related concept
Read the scenario before looking for a memorised answer.
✗
Dimension tables are normalized to minimize redundancy
Why it's wrong here
Dimension tables are denormalized to improve query performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that dimension tables should be normalized for data integrity, but in star schemas for BI, denormalization is intentional to optimize query performance over normalization.
Detailed technical explanation
How to think about this question
In a star schema, fact tables contain additive measures (e.g., sales amount, quantity) and foreign keys referencing dimension tables, while dimension tables are denormalized into a single table per business entity (e.g., product, customer) to enable star joins. Under the hood, this design minimizes the number of table scans and join operations, which is critical for aggregating billions of rows in BI tools like Power BI or Tableau. A real-world scenario: a sales fact table with 10 million rows joins to a denormalized product dimension table with 1,000 rows, avoiding a multi-table normalized path that would require three or more joins.
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: Dimension tables are denormalized to reduce the number of joins — Option B is correct because dimension tables in a star schema are intentionally denormalized to reduce the number of joins required for BI queries. This denormalization improves query performance by allowing fact tables to join directly to dimension tables without traversing multiple normalized tables, which is a key design principle for OLAP reporting.
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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