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