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Test your basic knowledge |
Data Warehousing
Start Test
Study First
Subject
:
it-skills
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
Match each statement with the correct term.
Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.
This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Tables containing summary records calculated from the main fact tables
Data mart design
Aggregate tables
Extract
Design
2. Data about the data
Operational BI
Metadata
Dimensional modelling
Choose the facts
3. Draws data from operational or external systems
Data mart bus architecture
Choose the facts
Independent data mart
Decision Support
4. Allows multiple CPUs to process multiple queries simultaneously - providing scalability.
OLAP
Load
Choose the dimensions
Parallel partitioning
5. Second stage of building a data mart
Define the users
Parallel partitioning
Conformed dimensions
Construction
6. Take data from a source system
Business Intelligence
Extract
Operational system
Load
7. A schema where dimensions are only connected to facts
Data mart
OLAP
Star schema
Load
8. Extract - Load and Transform
ELT
Business Intelligence
Transform
Dependent data mart
9. Used to support day-to-day operations
Conformed dimensions
Decision Support
Independent data mart
Operational BI
10. First stage of building a data mart
Constellation
Decision Support
Maintenance
Design
11. A summary record for a specific reporting period
Family
Aggregate tables
Snapshot
Metadata
12. A database table that maps (e.g.) manufacturing product IDs to sales product IDs
Data mart design
Translation table
Source system
Decision Support
13. A simplified form of a data warehouse supporting the work of a single line of business
Construction
Independent data marts
Front end
Data mart
14. Splitting data tables into smaller tables for efficient access.
ETL
Partioning
Metadata
Transaction
15. A group of conformed dimensions
Current rolling snapshot
Hub and spoke Architecture
Constellation
Dependent data mart
16. A table containing foreign keys and values
Star schema
OLAP
Fact table
Current rolling snapshot
17. Requesting more information about a particular fact
Load
Drilling down
Front end
Extract
18. Online Analytical Processing
Dimensional modelling
Population
Master Data Management
OLAP
19. A tool that allows users to write queries without having to learn SQL
Parallel processing and/or partitioning
Decision Support
Extract
Business query tool
20. Data accessible to the whole data warehouse
Population
Design
OLAP
Master data
21. Look-up tables referred to by fact tables
Construction
Dimension table
Parallel partitioning
Production report
22. An alternative term for a data warehouse
Analytical applications
Data mart design
Star schema
Enterprise Information Management
23. Import data into a new database
Parallel partitioning
Ad hoc query
Load
Fact grain
24. A report created by an IT department user
Enterprise Information Management
Independent data mart
Partioning
Production report
25. The operational systems - data warehouse and data marts
Transform
Back end
Fact grain
Data mart
26. Third stage of the data warehouse data architecture design process
Choose the dimensions
Partioning
Enterprise Information Management
Master Data Management
27. All database architectural fall into one of these two categories: EDW or _________ (independent data marts - data mart bus architecture - hub and spoke architecture - centralized data warehouse - federated data warehouse)
Master Data Management
Operational system
Data mart design
Source system
28. First stage of the data warehouse data architecture design process
Define the users
Operational system
Dimension table
Independent data mart
29. Unlike independent data marts the marts are linked via middleware
Back end
Front end
Metadata
Data mart bus architecture
30. Third stage of building a data mart
Fact table
Transaction
Population
Analytical applications
31. Fifth stage of building a data mart
Aggregate tables
Maintenance
Snowflake schema
Fact grain
32. Second stage of the data warehouse data architecture design process
Partioning
OLAP
Define the grain
Snowflake schema
33. A schema where dimensions may be connected to facts or to other dimensions
Transaction
Analytical applications
Snowflake schema
ETL
34. simplest and least costly architecture developed to operate independently of each other - poor solution
Star schema
Independent data marts
Data mart bus architecture
Dashboard
35. The business applications
Front end
Aggregate tables
Business Intelligence
ETL
36. A visual display of the most important information needed by a particular user
OLAP
Dimensional modelling
Dashboard
Front end
37. Fourth stage of building a data mart
Constellation
Accessing
Production report
Data mart
38. Fourth stage of the data warehouse data architecture design process
Star schema
Choose the facts
Data mart design
ETL
39. Business applications - methods and tools that support the caputre and use of master data
Back end
ETL
Parallel processing and/or partitioning
Master Data Management
40. A query created by a BI user
Dashboard
Operational system
Ad hoc query
Extract
41. Dimensions which are shared by two or more facts
Analytical applications
Fact grain
Population
Conformed dimensions
42. A logical design technique that presents data in a way that is optimised for high-performance access
Dimensional modelling
Define the users
Back end
Partioning
43. Extract - Transform and Load
Constellation
ETL
Fact table
Analytical applications
44. A computer system used to support the day-to-day operations of an organisation
ELT
Snapshot
Operational system
Load
45. The level of detail required for a fact to be useful
Define the users
Business query tool
Fact grain
Analytical applications
46. An operational system that provides data for a business information system
Source system
Ad hoc query
Dependent data mart
Metadata
47. Make changes to data so that it is compatible with a new database
Transform
Population
Fact grain
Parallel processing and/or partitioning
48. A daily snapshot for each day of a set period
Current rolling snapshot
Construction
Accessing
Analytical applications
49. Most famous data warehouse where a maintable infastructure includes a centralized data warehouse that serves for the needs of all organizational units (most favored 39 percent)
Master Data Management
Data mart design
Family
Hub and spoke Architecture
50. Requesting information about related facts
Design
Transform
Drilling across
Independent data marts