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Oracle Data Warehousing 11g Essentials

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Answer 13 questions in 15 minutes.
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Match each statement with the correct term.
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1. List 5 high level Data Warehouse I/O considerations
Dimension tables - also known as lookup or reference tables - contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive a
Configure I/O for Bandwidth not Capacity - Stripe Far and Wide - Use Redundancy - Test the I/O System Before Building the Database - Plan for Growth
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
The purpose of a materialized view is to increase query execution performance - The existence of a materialized view is transparent to SQL applications - A materialized view consumes storage space - The contents of the materialized view must be u

2. List the three types of materialized views
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
Materialized views with aggregates - Materialized views with only joins - Nested materialized views
Dimension tables - also known as lookup or reference tables - contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive a
Subject Oriented - Integrated - Nonvolatile - Time Variant

3. What is a data mart?
Fact tables are the large tables in your data warehouse schema that store business measurements. Fact tables typically contain facts and foreign keys to the dimension tables. Fact tables represent data - usually numeric and additive - that can be ana
The purpose of a materialized view is to increase query execution performance - The existence of a materialized view is transparent to SQL applications - A materialized view consumes storage space - The contents of the materialized view must be u
A data warehouse that is designed for a particular line of business - such as sales - marketing - or finance. In a dependent data mart - the data can be derived from an enterprise-wide data warehouse. In an independent data mart - data can be collect
Dimension tables - also known as lookup or reference tables - contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive a

4. Describe dimension tables in a Data Warehouse
Dimension tables - also known as lookup or reference tables - contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive a
Fact tables are the large tables in your data warehouse schema that store business measurements. Fact tables typically contain facts and foreign keys to the dimension tables. Fact tables represent data - usually numeric and additive - that can be ana
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
The purpose of a materialized view is to increase query execution performance - The existence of a materialized view is transparent to SQL applications - A materialized view consumes storage space - The contents of the materialized view must be u

5. List 4 characteristics of a data warehouse as set forth by William Inmon
Tablespaces - Tables and Partitioned Tables - Views - Integrity Constraints - Dimensions - Indexes and Partitioned Indexes - Materialized Views
Automatic Storage Management handles the tasks of striping and providing disk redundancy - including rebalancing the database files when new disks are added to the system.
Subject Oriented - Integrated - Nonvolatile - Time Variant
Summary table - Aggregate table

6. List Oracle Data Mining functions
Tablespaces - Tables and Partitioned Tables - Views - Integrity Constraints - Dimensions - Indexes and Partitioned Indexes - Materialized Views
The purpose of a materialized view is to increase query execution performance - The existence of a materialized view is transparent to SQL applications - A materialized view consumes storage space - The contents of the materialized view must be u
Summary table - Aggregate table
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction

7. List two alternative names for Materialized Views in Data Warehousing theory
Dimension tables - also known as lookup or reference tables - contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive a
Summary table - Aggregate table
A data warehouse that is designed for a particular line of business - such as sales - marketing - or finance. In a dependent data mart - the data can be derived from an enterprise-wide data warehouse. In an independent data mart - data can be collect
Statistical functions - OLAP - Data Mining

8. In what respects does a materialized view behaves like an index?
Fact tables are the large tables in your data warehouse schema that store business measurements. Fact tables typically contain facts and foreign keys to the dimension tables. Fact tables represent data - usually numeric and additive - that can be ana
Summary table - Aggregate table
The purpose of a materialized view is to increase query execution performance - The existence of a materialized view is transparent to SQL applications - A materialized view consumes storage space - The contents of the materialized view must be u
A data warehouse that is designed for a particular line of business - such as sales - marketing - or finance. In a dependent data mart - the data can be derived from an enterprise-wide data warehouse. In an independent data mart - data can be collect

9. List some physicla structures of a Data Warehouse
Tablespaces - Tables and Partitioned Tables - Views - Integrity Constraints - Dimensions - Indexes and Partitioned Indexes - Materialized Views
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data - but can include data from other sources. Data warehouses sep
Materialized views with aggregates - Materialized views with only joins - Nested materialized views
Statistical functions - OLAP - Data Mining

10. What is a Data Warehouse?
A data warehouse that is designed for a particular line of business - such as sales - marketing - or finance. In a dependent data mart - the data can be derived from an enterprise-wide data warehouse. In an independent data mart - data can be collect
Configure I/O for Bandwidth not Capacity - Stripe Far and Wide - Use Redundancy - Test the I/O System Before Building the Database - Plan for Growth
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data - but can include data from other sources. Data warehouses sep
Summary table - Aggregate table

11. List 3 ways (high level) to extract information from a data warehouse
Summary table - Aggregate table
Materialized views with aggregates - Materialized views with only joins - Nested materialized views
Statistical functions - OLAP - Data Mining
Subject Oriented - Integrated - Nonvolatile - Time Variant

12. What is the main advantage in using ASM?
Tablespaces - Tables and Partitioned Tables - Views - Integrity Constraints - Dimensions - Indexes and Partitioned Indexes - Materialized Views
Statistical functions - OLAP - Data Mining
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
Automatic Storage Management handles the tasks of striping and providing disk redundancy - including rebalancing the database files when new disks are added to the system.

13. Describe fact tables in a Data Warehouse
Automatic Storage Management handles the tasks of striping and providing disk redundancy - including rebalancing the database files when new disks are added to the system.
Summary table - Aggregate table
Fact tables are the large tables in your data warehouse schema that store business measurements. Fact tables typically contain facts and foreign keys to the dimension tables. Fact tables represent data - usually numeric and additive - that can be ana
Materialized views with aggregates - Materialized views with only joins - Nested materialized views