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