A marketing analytics team is building a data model to analyze campaign performance. They have a fact table of campaign clicks and dimensions for campaign, channel, and date. The campaign dimension has attributes such as campaign name, budget, and target audience. The team wants to minimize query complexity and maximize query performance for their analysts, who are not familiar with complex joins. Which schema design should they use?

Snowflake schema with normalized dimension tables

Data vault model with hubs, links, and satellites

Star schema with denormalized dimension tables

Relational model with fully normalized tables

A data modeler is designing a schema for a sales analytics application. The fact table contains sales transactions, and there are dimensions for product, customer, and store. The product dimension has a hierarchy of category and subcategory, and the customer dimension has a hierarchy of region and country. The modeler wants to reduce data redundancy and make it easier to update hierarchy attributes independently. Which schema design should they use?

Data vault model with hubs, links, and satellites

Star schema with denormalized dimensions

Snowflake schema with normalized dimensions

Relational model with fully normalized tables

When would you typically choose a star schema over a snowflake schema?

When you need to minimize data redundancy at all costs

When you want to maximize query performance and simplicity for business users

When you have a highly normalized operational database

When you need to track historical changes in dimension attributes

Snowflake SnowPro Advanced:Data Analyst

Domain 3Objective 2

Design and Use Data Models SNOWPRO-ADVANCED-DATA-ANALYST Practice Questions (Page 5)

Part of the Data Transformation and Data Modeling domain, which accounts for 23% of the SNOWPRO-ADVANCED-DATA-ANALYST exam.

24questions here

5free pages

6concepts

23%of the exam

Questions 21–24

21application · medium
A marketing analytics team is building a data model to analyze campaign performance. They have a fact table of campaign clicks and dimensions for campaign, channel, and date. The campaign dimension has attributes such as campaign name, budget, and target audience. The team wants to minimize query complexity and maximize query performance for their analysts, who are not familiar with complex joins. Which schema design should they use?
Select an answer first
22application · medium
A data modeler is designing a schema for a sales analytics application. The fact table contains sales transactions, and there are dimensions for product, customer, and store. The product dimension has a hierarchy of category and subcategory, and the customer dimension has a hierarchy of region and country. The modeler wants to reduce data redundancy and make it easier to update hierarchy attributes independently. Which schema design should they use?
Select an answer first
23foundation · easy
When would you typically choose a star schema over a snowflake schema?
Select an answer first
24foundation · easy
Which Snowflake object is a virtual table that is defined by a query and does not store data physically?
Select an answer first

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Design and Use Data Models SNOWPRO-ADVANCED-DATA-ANALYST Practice Questions (Page 5)

Questions 21–24

When would you typically choose a star schema over a snowflake schema?

Which Snowflake object is a virtual table that is defined by a query and does not store data physically?

Keep studying — Data Transformation and Data Modeling