Quick Answer
The answer is a star schema, because this design places a central fact table recording each sale transaction with foreign keys to separate dimension tables for product, store, and date. The fact table holds quantitative measures like quantity sold, while the dimension tables store descriptive attributes such as product name, category, and price, which is the hallmark of a star schema. On the CompTIA Data+ DA0-001 exam, this scenario tests your ability to recognize the separation of facts and dimensions for OLAP optimization, often appearing in questions about data warehouse modeling. A common trap is confusing this with a snowflake schema, but remember that a star schema keeps dimension tables denormalized—no further sub-dimensions. For a quick memory tip, think of the fact table as the sun and dimension tables as its orbiting planets, all directly connected for fast query performance.
This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. 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 data engineer is designing a data warehouse for a retail company. The fact table must record each sale transaction, including product ID, store ID, date, and quantity sold. The product details (name, category, price) are stored in a separate table. This design is an example of which data modeling concept?
Star schema
Correct: fact table linked to dimension tables.
Data lake
Why wrong: Data lake stores raw data, not structured schema.
Normalization
Why wrong: Normalization is a broader process, not a specific schema.
Snowflake schema
Why wrong: Snowflake would further normalize product dimension into sub-dimensions.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Star schema
This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Star schema
Why this is correct
Correct: fact table linked to dimension tables.
Related concept
Read the scenario before looking for a memorised answer.
Data lake
Why it's wrong here
Data lake stores raw data, not structured schema.
Normalization
Why it's wrong here
Normalization is a broader process, not a specific schema.
Snowflake schema
Why it's wrong here
Snowflake would further normalize product dimension into sub-dimensions.
Common exam traps
The trap here is that candidates confuse star schema with snowflake schema, but the key differentiator is whether dimension tables are further normalized (snowflake) or kept denormalized (star), and this question's single product table clearly indicates a star schema.
Detailed technical explanation
In a star schema, the fact table typically uses a composite primary key made up of foreign keys from each dimension, enabling fast aggregations via bitmap indexes or star joins. The denormalized dimension tables reduce the number of joins needed for queries, which is critical in data warehouses handling billions of rows. A real-world scenario is a retail chain analyzing daily sales by product category; the star schema allows a single join between the fact table and the product dimension to compute total revenue per category without traversing multiple normalized tables.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Star schema — This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial services company is migrating its customer data from a legacy on-premises relational database to a cloud-based data warehouse. The legacy database uses a denormalized schema with a single table 'customer_master' that contains all customer attributes, including repeated groups for multiple accounts per customer (account1_type, account1_balance, account2_type, account2_balance, etc.). The data warehouse team wants to implement a normalized star schema with separate dimension and fact tables. During the ETL process, the team encounters an error: 'Data truncation: string data right truncation' when loading account_type values into the dim_account table. The account_type column in dim_account is defined as VARCHAR(10), but the source data contains account types like 'SavingsPlus' (11 characters) and 'CheckingPremium' (15 characters). The team must resolve this issue without losing data. Which course of action should the team take?
hardWhy D: Option D is correct because increasing the VARCHAR length of dim_account.account_type to accommodate the longest account type (e.g., VARCHAR(15) for 'CheckingPremium') resolves the data truncation error without data loss. This aligns with the star schema design principle of preserving source data integrity while ensuring the column definition matches the actual data length. The team must avoid truncation or NULL insertion to maintain accurate dimensional attributes for analytics.
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Last reviewed: Jun 11, 2026
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