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