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