Hierarchies & Snowflakes

What does the attribute hierarchy provide in the context of data warehousing?

In data warehousing, what does it mean to 'line up all the hierarchies for a given star on a single diagram'?

Which schema consists of a fact table and a group of dimension tables, with each dimension table joined directly to the fact table?

What is the result called when the relationships between dimension attributes are made explicit in a dimensional design?

How does snowflaking a dimension relate to normalization in database design?

In the context of data warehousing, what is the primary drawback of using normalization techniques like snowflaking?

What is a key characteristic of dimensional models in relation to relational databases?

What is the purpose of precomputing the various combinations of dimension values and fact values in a cube?

What is a primary advantage of using a multidimensional database compared to a relational database for data warehousing?

Which term describes the process of interactively changing the perspective on data, such as adding or removing attributes, and receiving immediate feedback?

In the context of OLAP, what does it mean to 'slice and dice' data?

How does interacting with a star schema typically differ from interacting with a cube in a multidimensional database?

Which of the following describes the primary function of ETL in data warehousing?

In a star schema loading process, what is the significance of loading dimension tables before fact tables?

What is the first step in loading dimension tables during the ETL process?

What is the purpose of surrogate keys in dimension tables?

What is the primary goal of processing fact tables during the ETL process?

What step is essential before loading records into the fact table?

Which operation is performed on facts to ensure they match the grain of the fact table?

Which of the following is a key characteristic of OLAP interaction with a cube?

What is the significance of 'normalization' in database design?

Which of the following steps is a standard practice as a project is loading dimension tables?

In the context of data warehousing, what does 'Drilling' generally refer to?

Which statement best explains why normalization is typically avoided in analytical databases?

What is a significant advantage to dimensional models being deployed on Multi-dimensional Databases?

If you're 'slicing and dicing' a data cube, what are you trying to accomplish?

What is a core function of the 'Fact' table in a star schema?

In a star schema, what is the primary role of the dimension tables?

During ETL, why is it useful to 'extract source data from source(s)'?

During dimensional modelling processes, what are 'surrogate keys' used for?

What could possibly cause an ETL process to fail during the Fact Table Processing?

Given that you are utilizing a star schema in your data warehouse, which action would be the MOST critical during the data loading process?

Suppose you need to represent 'order dollars' in your cube, but your source data tracks orders at the product and day level. What do you need to consider?

You've implemented a data warehouse with a star schema but notice query performance is degrading. Which design change would MOST quickly help?

You're tasked with designing an ETL process for a data warehouse. Your source system contains historical snapshots of customer data which are slowly changing, but you're only tracking the very latest version. Which approach is MOST suitable for loading dimension tables?

Which of the following represents the correct order of operations in a typical ETL process?

What does the acronym MDB stand for in the context of data warehousing?

In data warehousing, what is meant by a 'cube'?