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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. Draws data from operational or external systems
Independent data mart
Snowflake schema
Star schema
Drilling down
2. A group of conformed dimensions
Construction
Define the grain
Constellation
Star schema
3. Extract - Load and Transform
Parallel partitioning
Operational system
Partioning
ELT
4. A tool that allows users to write queries without having to learn SQL
Business query tool
Design
Ad hoc query
Enterprise Information Management
5. Fourth stage of the data warehouse data architecture design process
Family
Extract
Define the grain
Choose the facts
6. Second stage of the data warehouse data architecture design process
Define the grain
Business Intelligence
Conformed dimensions
Data mart design
7. Dimensions which are shared by two or more facts
Data mart bus architecture
Conformed dimensions
Partioning
Fact grain
8. Extract - Transform and Load
ETL
Drilling across
Dependent data mart
Metadata
9. Issues to consider when deciding EDW architecture: 1. Which database management system (DBMS) should be used? (ex: Crash Data" MS SQL) 2. Will ______________________ be used? 3. Will data migration tools be used to load the data warehouse?4. What too
Metadata
Parallel processing and/or partitioning
Decision Support
Partioning
10. A group of related fact tables
Production report
Family
Star schema
Accessing
11. Unlike independent data marts the marts are linked via middleware
Analytical applications
Drilling across
Data mart bus architecture
OLAP
12. First stage of the data warehouse data architecture design process
Dashboard
Conformed dimensions
Define the users
ETL
13. A system that allows people to access and analyse data for business management and performance improvement
Parallel partitioning
Business Intelligence
Dimensional modelling
Operational system
14. Splitting data tables into smaller tables for efficient access.
Partioning
Fact table
Dependent data mart
Parallel processing and/or partitioning
15. The business applications
Aggregate tables
Front end
Data mart
Drilling across
16. The level of detail required for a fact to be useful
Constellation
Transaction
Fact grain
Dependent data mart
17. Look-up tables referred to by fact tables
Dimension table
Accessing
Dimensional modelling
Parallel partitioning
18. Fourth stage of building a data mart
Population
Decision Support
Accessing
Metadata
19. A schema where dimensions may be connected to facts or to other dimensions
Drilling across
Accessing
Star schema
Snowflake schema
20. A query created by a BI user
Business query tool
Back end
Ad hoc query
Fact table
21. Data about the data
ELT
Business Intelligence
Metadata
Constellation
22. Online Analytical Processing
OLAP
Partioning
ELT
Define the grain
23. An operational system that provides data for a business information system
Dependent data mart
Source system
Define the grain
Transaction
24. Take data from a source system
Data mart
Snowflake schema
Extract
Transform
25. A simplified form of a data warehouse supporting the work of a single line of business
Dimension table
Business Intelligence
Data mart
Front end
26. Draws data from a data warehouse
Drilling down
Extract
Transaction
Dependent data mart
27. 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)
Hub and spoke Architecture
ETL
Decision Support
Aggregate tables
28. A table containing foreign keys and values
Operational system
Aggregate tables
Choose the dimensions
Fact table
29. Business applications - methods and tools that support the caputre and use of master data
Master data
Front end
Define the grain
Master Data Management
30. A single low-level fact representing a single business operation
Load
Star schema
Transaction
Analytical applications
31. Requesting more information about a particular fact
Drilling down
Choose the dimensions
Fact grain
Population
32. Make changes to data so that it is compatible with a new database
Maintenance
Population
Transform
Data mart
33. First stage of building a data mart
Back end
Fact grain
Design
Choose the dimensions
34. Used to support day-to-day operations
Operational BI
Snowflake schema
Fact grain
Hub and spoke Architecture
35. A computer system used to support the day-to-day operations of an organisation
ELT
Source system
Operational system
Parallel partitioning
36. A daily snapshot for each day of a set period
Independent data mart
Current rolling snapshot
Production report
Constellation
37. A schema where dimensions are only connected to facts
Define the users
Front end
Star schema
ELT
38. The operational systems - data warehouse and data marts
Front end
Accessing
Load
Back end
39. Third stage of the data warehouse data architecture design process
Data mart bus architecture
Enterprise Information Management
Choose the dimensions
Transform
40. A synonym for Business Intelligence
Design
Conformed dimensions
Decision Support
Constellation
41. Tables containing summary records calculated from the main fact tables
Aggregate tables
Construction
Operational system
Business Intelligence
42. An alternative term for a data warehouse
Dashboard
Partioning
Enterprise Information Management
Snapshot
43. Requesting information about related facts
Drilling across
Fact grain
Parallel partitioning
Independent data marts
44. Allows multiple CPUs to process multiple queries simultaneously - providing scalability.
Fact grain
Production report
Parallel partitioning
Independent data marts
45. simplest and least costly architecture developed to operate independently of each other - poor solution
Independent data marts
Metadata
Current rolling snapshot
Data mart bus architecture
46. A report created by an IT department user
Enterprise Information Management
Production report
Drilling across
Front end
47. A visual display of the most important information needed by a particular user
Metadata
Parallel partitioning
Independent data mart
Dashboard
48. Import data into a new database
Fact grain
Snapshot
Load
OLAP
49. Third stage of building a data mart
Population
Metadata
Load
Snapshot
50. These extend BI into automating and optimising processes
Conformed dimensions
Operational system
Fact table
Analytical applications