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