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