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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 - Transform and Load
Choose the dimensions
Business Intelligence
ETL
Master data
2. A visual display of the most important information needed by a particular user
Operational system
Design
Dashboard
Translation table
3. First stage of the data warehouse data architecture design process
Fact table
Load
Define the users
Define the grain
4. Dimensions which are shared by two or more facts
Snapshot
Conformed dimensions
Aggregate tables
Ad hoc query
5. Fifth stage of building a data mart
Independent data marts
Maintenance
Construction
Dashboard
6. 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
Independent data marts
OLAP
Partioning
7. Requesting more information about a particular fact
Drilling down
Conformed dimensions
Population
Snapshot
8. Second stage of the data warehouse data architecture design process
Snapshot
Define the grain
Snowflake schema
Current rolling snapshot
9. Extract - Load and Transform
Fact grain
ELT
Snowflake schema
Hub and spoke Architecture
10. Allows multiple CPUs to process multiple queries simultaneously - providing scalability.
Parallel partitioning
Independent data marts
Constellation
Operational system
11. A computer system used to support the day-to-day operations of an organisation
Enterprise Information Management
Transform
Operational system
Construction
12. A schema where dimensions may be connected to facts or to other dimensions
Partioning
Snowflake schema
Snapshot
Enterprise Information Management
13. Requesting information about related facts
Back end
Define the users
Drilling across
Fact table
14. Third stage of the data warehouse data architecture design process
Define the grain
Choose the dimensions
Snowflake schema
Decision Support
15. Third stage of building a data mart
Star schema
Constellation
Construction
Population
16. A group of related fact tables
Dimension table
Operational system
Partioning
Family
17. Make changes to data so that it is compatible with a new database
Enterprise Information Management
Transform
Hub and spoke Architecture
Translation table
18. A synonym for Business Intelligence
OLAP
Star schema
Parallel partitioning
Decision Support
19. These extend BI into automating and optimising processes
Population
Analytical applications
Ad hoc query
Source system
20. Unlike independent data marts the marts are linked via middleware
Source system
Translation table
Drilling down
Data mart bus architecture
21. Splitting data tables into smaller tables for efficient access.
Analytical applications
Independent data marts
Partioning
Back end
22. A logical design technique that presents data in a way that is optimised for high-performance access
Dependent data mart
Production report
Dimensional modelling
Snowflake schema
23. An alternative term for a data warehouse
Enterprise Information Management
Dependent data mart
Analytical applications
Parallel processing and/or partitioning
24. A group of conformed dimensions
Data mart
Dimension table
Constellation
Star schema
25. Take data from a source system
Construction
Current rolling snapshot
Data mart design
Extract
26. Data accessible to the whole data warehouse
Enterprise Information Management
Metadata
Master data
Transform
27. Business applications - methods and tools that support the caputre and use of master data
Dimensional modelling
Independent data marts
Master Data Management
Drilling down
28. Look-up tables referred to by fact tables
ELT
Parallel partitioning
Dimension table
Define the grain
29. A summary record for a specific reporting period
Dimension table
Extract
Back end
Snapshot
30. A query created by a BI user
Extract
Constellation
Ad hoc query
Business Intelligence
31. 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)
Data mart design
Business Intelligence
Operational BI
Data mart
32. The level of detail required for a fact to be useful
Source system
Analytical applications
Dashboard
Fact grain
33. Draws data from operational or external systems
Hub and spoke Architecture
Operational system
Independent data mart
Drilling across
34. A single low-level fact representing a single business operation
Transaction
Metadata
Data mart design
Extract
35. A system that allows people to access and analyse data for business management and performance improvement
Business Intelligence
OLAP
Translation table
Choose the facts
36. Used to support day-to-day operations
Choose the facts
Operational BI
Master Data Management
Data mart bus architecture
37. A table containing foreign keys and values
Fact table
Family
Choose the dimensions
Conformed dimensions
38. simplest and least costly architecture developed to operate independently of each other - poor solution
Drilling down
Independent data marts
Transaction
Data mart bus architecture
39. A database table that maps (e.g.) manufacturing product IDs to sales product IDs
Construction
Transform
Translation table
Accessing
40. Draws data from a data warehouse
Data mart
Family
Transform
Dependent data mart
41. The operational systems - data warehouse and data marts
Star schema
Independent data mart
Load
Back end
42. Tables containing summary records calculated from the main fact tables
Aggregate tables
Ad hoc query
Snowflake schema
Star schema
43. A schema where dimensions are only connected to facts
Load
Extract
Star schema
Maintenance
44. Fourth stage of building a data mart
Hub and spoke Architecture
Accessing
ELT
Family
45. Online Analytical Processing
Front end
OLAP
ETL
Metadata
46. Second stage of building a data mart
Construction
Hub and spoke Architecture
Current rolling snapshot
Dashboard
47. 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)
Extract
Construction
Aggregate tables
Hub and spoke Architecture
48. Fourth stage of the data warehouse data architecture design process
Fact grain
Choose the facts
Operational system
Define the users
49. A simplified form of a data warehouse supporting the work of a single line of business
Operational system
Constellation
Choose the facts
Data mart
50. Import data into a new database
Master data
Load
Accessing
Snapshot