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