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