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