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