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