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