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