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