A retail company wants to analyze daily sales by store, product, and date. They have a fact table with one row per product sold per transaction. Analysts frequently query total revenue by store and by product. Which schema design best supports these queries with minimal joins?

Snowflake schema with normalized dimension tables for store, product, and date

Data vault schema with hubs, links, and satellites for each business concept

A single wide table with all sales attributes and measures

Star schema with a sales fact table and separate dimension tables for store, product, and date

When designing analytical tables in Databricks SQL, which practice is considered a best practice for performance?

Avoiding the use of Delta Lake to reduce complexity.

Storing all data in a single large table without partitioning.

Using external tables without any optimization.

Using Delta Lake with partitioning on frequently filtered columns.

What is the primary purpose of the data vault modeling approach?

To enforce strict referential integrity for transactional systems.

To simplify queries for end users by providing a denormalized structure.

To provide a highly normalized schema that minimizes storage usage.

To enable agile and flexible data warehousing that can adapt to changing business requirements.

What is the primary purpose of using a star schema in analytical workloads?

To provide a flexible schema that can accommodate frequent structural changes without impacting queries.

To ensure transactional integrity through foreign key constraints and ACID compliance.

To simplify queries and improve query performance by reducing the number of joins needed.

To minimize data redundancy by normalizing all tables to third normal form.

A data team must choose between a star and a snowflake schema for a new reporting solution. The business requires fast query response times for dashboards, but the team also wants to minimize maintenance effort for dimension updates. Which choice best balances these needs?

Data vault schema, because it is the most flexible

Snowflake schema, because it reduces data redundancy

A hybrid schema with some normalized and some denormalized dimensions

Star schema, because it requires fewer joins and is easier to maintain

DatabricksCertified Data Analyst Associate

Domain 8Objective 1

Apply Industry-Standard Data Modeling Techniques, Such as Star, Snowflake, and Data Vault Schemas, to Analytical Workloads. DATA-ANALYST-ASSOCIATE Practice Questions (Page 1)

Part of the Section 8: Data Modeling with Databricks SQL domain, which makes up ~5% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~2–3 in this domain), expect 1–2 from this objective — we provide 21 practice questions to prepare you well beyond it. (estimate)

21questions here

5free pages

7concepts

Questions 1–5

1application · medium
A retail company wants to analyze daily sales by store, product, and date. They have a fact table with one row per product sold per transaction. Analysts frequently query total revenue by store and by product. Which schema design best supports these queries with minimal joins?
Select an answer first
2foundation · easy
When designing analytical tables in Databricks SQL, which practice is considered a best practice for performance?
Select an answer first
3foundation · easy
What is the primary purpose of the data vault modeling approach?
Select an answer first
4foundation · easy
What is the primary purpose of using a star schema in analytical workloads?
Select an answer first
5application · medium
A data team must choose between a star and a snowflake schema for a new reporting solution. The business requires fast query response times for dashboards, but the team also wants to minimize maintenance effort for dimension updates. Which choice best balances these needs?
Select an answer first

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