Test your basic knowledge |

Oracle Data Warehousing 11g Essentials

Instructions:

Answer 13 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. List 3 ways (high level) to extract information from a data warehouse
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
Statistical functions - OLAP - Data Mining
Subject Oriented - Integrated - Nonvolatile - Time Variant
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

2. What is a data mart?
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
Materialized views with aggregates - Materialized views with only joins - Nested materialized views
Subject Oriented - Integrated - Nonvolatile - Time Variant

3. List Oracle Data Mining functions
Subject Oriented - Integrated - Nonvolatile - Time Variant
Summary table - Aggregate table
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
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction

4. List 5 high level Data Warehouse I/O considerations
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
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 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
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

5. List 4 characteristics of a data warehouse as set forth by William Inmon
Subject Oriented - Integrated - Nonvolatile - Time Variant
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
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
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

6. Describe fact tables in a Data Warehouse
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
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
Subject Oriented - Integrated - Nonvolatile - Time Variant
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

7. In what respects does a materialized view behaves like an index?
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 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
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

8. List some physicla structures of a Data Warehouse
Tablespaces - Tables and Partitioned Tables - Views - Integrity Constraints - Dimensions - Indexes and Partitioned Indexes - Materialized Views
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
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

9. List two alternative names for Materialized Views in Data Warehousing theory
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
Statistical functions - OLAP - Data Mining
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

10. List the three types of materialized views
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
Materialized views with aggregates - Materialized views with only joins - Nested materialized views

11. What is the main advantage in using ASM?
Materialized views with aggregates - Materialized views with only joins - Nested materialized views
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
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.

12. What is a Data Warehouse?
Materialized views with aggregates - Materialized views with only joins - Nested 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
Subject Oriented - Integrated - Nonvolatile - Time Variant
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

13. Describe dimension tables in a Data Warehouse
Subject Oriented - Integrated - Nonvolatile - Time Variant
Classification - Regression - Anomaly Detection - Attribute Importance - Clustering - Associations - Feature Extraction
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