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