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