Teradata Teradata Data Warehousing 2

Question 1

What is a snowflake schema and how does it differ from a star schema?

Accepted Answer

A snowflake schema normalizes dimension tables into sub-dimensions, creating more joins than a star schema

Answer

A snowflake schema normalizes dimension tables into multiple related tables (e.g., splitting Customer into Customer and City), reducing redundancy but requiring more joins.

Question 2

What is the purpose of aggregate tables in a Teradata data warehouse?

Accepted Answer

Store pre-computed summary results to speed up common analytical queries

Answer

Aggregate tables contain pre-calculated summaries (e.g., monthly sales totals by region) so that analytical queries can read the small aggregate rather than scanning the entire fact table.

Question 3

In Teradata, what is row partitioning by date used for in a data warehouse?

Accepted Answer

Enables partition elimination so queries with date filters scan only relevant partitions

Answer

Partitioning a large fact table by date means queries with a date-range filter can skip entire partitions (partition elimination), dramatically reducing I/O.

Question 4

What does OLAP stand for and what is its role in Teradata data warehousing?

Accepted Answer

Online Analytical Processing — supports complex multi-dimensional analysis of large historical datasets

Answer

OLAP systems, including Teradata-based warehouses, support complex analytical queries involving aggregations, trend analysis, and multi-dimensional data slicing over large historical datasets.

Question 5

What is a conformed dimension in data warehousing?

Accepted Answer

A dimension table shared and consistently defined across multiple fact tables or subject areas

Answer

A conformed dimension has standardized definitions and values that are shared across multiple fact tables, enabling consistent cross-subject-area analysis (e.g., a shared Date dimension).

Teradata Practice Test

Teradata Practice Test

Teradata Teradata Data Warehousing 2