Welcome to the The Physical Design Process MCQs Page

Dive deep into the fascinating world of The Physical Design Process with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of The Physical Design Process, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of The Physical Design Process, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Warehousing and OLAP.

Check out the MCQs below to embark on an enriching journey through The Physical Design Process. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Warehousing and OLAP.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of The Physical Design Process. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

The Physical Design Process MCQs | Page 7 of 9

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Important Terms and Definitions

Q61.

What is a disadvantage of bitmapped indexes?

They require less space compared to B-Tree indexes

They are suitable for high-selectivity columns

They do not need table access after index access

They need reconstruction when new values are introduced for the indexed columns

Discuss

Answer: (d).They need reconstruction when new values are introduced for the indexed columns Explanation:A disadvantage of bitmapped indexes is that they need reconstruction when new values are introduced for the indexed columns.

Q62.

In a data warehouse environment, when are bitmapped indexes more suitable than B-Tree indexes?

When the data warehouse deals with small volumes of data

When the queries involve high-selectivity columns

When analysis involves "what-if" scenarios with multiple predicates

When data tables require frequent updates

Discuss

Answer: (c).When analysis involves "what-if" scenarios with multiple predicates Explanation:Bitmapped indexes are more suitable in a data warehouse environment when analysis involves "what-if" scenarios with multiple predicates.

Q63.

What is a recommended indexing strategy for dimension tables in a data warehouse?

Create a B-Tree index only on the columns used in join conditions

Create a bitmapped index on the primary key column

Create a unique B-Tree index on the single-column primary key

Avoid indexing dimension tables to prevent performance issues

Discuss

Answer: (c).Create a unique B-Tree index on the single-column primary key Explanation:A recommended indexing strategy for dimension tables in a data warehouse is to create a unique B-Tree index on the single-column primary key.

Q64.

When considering multicolumn indexes for dimension tables, what should be considered for arranging columns?

Arrange columns based on alphabetical order

Arrange columns randomly

Place the columns more frequently accessed or at higher hierarchical levels at the high order

Place the columns with low selectivity at the high order

Discuss

Answer: (c).Place the columns more frequently accessed or at higher hierarchical levels at the high order Explanation:When considering multicolumn indexes for dimension tables, it's advisable to place the columns that are more frequently accessed or at higher hierarchical levels at the high order of the multicolumn indexes.

Q65.

Which technique enables more data to be loaded into a single block, improving data retrieval performance?

Data partitioning

Table merging

Regular purging of data

Physical data compacting

Discuss

Answer: (d).Physical data compacting Explanation:Physical data compacting enables more data to be loaded into a single block, improving data retrieval performance.

Q66.

What is a key advantage of data partitioning in a data warehouse?

Reducing the need for backup and recovery

Eliminating the need for indexing

Accelerating the loading of large tables

Simplifying the process of purging and archiving records

Discuss

Answer: (c).Accelerating the loading of large tables Explanation:A key advantage of data partitioning in a data warehouse is accelerating the loading of large tables.

Q67.

Why is planning for partitioning crucial in a data warehouse?

It helps in creating unique B-Tree indexes

It simplifies the process of regular purging

It enables table merging for performance improvement

Doing this after the data warehouse is deployed is time-consuming and difficult

Discuss

Answer: (d).Doing this after the data warehouse is deployed is time-consuming and difficult Explanation:Planning for partitioning is crucial in a data warehouse because doing this after the data warehouse is deployed is time-consuming and difficult.

Q68.

What is the primary benefit of partitioning a table in a data warehouse?

Eliminates the need for indexing

Accelerates the loading of large tables

Simplifies the process of regular purging

Reduces the need for backup and recovery

Discuss

Answer: (b).Accelerates the loading of large tables Explanation:The primary benefit of partitioning a table in a data warehouse is that it accelerates the loading of large tables.

Q69.

In vertical partitioning of a table, how are partitions separated?

By grouping selected rows together

By grouping selected columns together

By dividing the table based on calendar dates

By separating the recent events and past history

Discuss

Answer: (b).By grouping selected columns together Explanation:In vertical partitioning of a table, partitions are separated by grouping selected columns together.

Q70.

What is a benefit of horizontal partitioning in a data warehouse?

Faster index building

Improved data clustering

Reduced downtime during data corruption

Sequential access of related data

Discuss

Answer: (d).Sequential access of related data Explanation:A benefit of horizontal partitioning in a data warehouse is sequential access of related data.

Page 7 of 9

Building Blocks of Data Warehouse 🔒

Trends in Data Warehousing 🔒

Planning and Project Management 🔒

Requirements as the Driving Force for Data warehousing 🔒

Architectural Components 🔒

Infrastructure as the Foundation for Data Warehousing 🔒

Role of Metadata in Data Warehousing 🔒

Advanced Topics in Dimensional Modeling 🔒

Data Extraction,Transformation and Loading 🔒

OLAP in the Data Warehouse 🔒

Data Warehousing and the Web 🔒

Data Mining Basics 🔒

Data Warehouse Deployment 🔒

Welcome to the The Physical Design Process MCQs Page

The Physical Design Process MCQs | Page 7 of 9

Explore more Topics under Data Warehousing and OLAP

The Compelling Need for Data Warehousing

Defining the Business Requirements

Principles of Dimensional Modeling

Importance of Data Quality

Matching Information to the Classes of Users

Growth and Maintenance

Important Terms and Definitions

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