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