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