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