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