Welcome to the Trends in Data Warehousing MCQs Page

Dive deep into the fascinating world of Trends in Data Warehousing with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Trends in Data Warehousing, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Trends in Data Warehousing, 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 Trends in Data Warehousing. 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 Trends in Data Warehousing. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Trends in Data Warehousing MCQs | Page 2 of 12

Explore more Topics under Data Warehousing and OLAP

01

The Compelling Need for Data Warehousing

02

Building Blocks of Data Warehouse 🔒

03

Planning and Project Management 🔒

04

Defining the Business Requirements

05

Requirements as the Driving Force for Data warehousing 🔒

06

Architectural Components 🔒

07

Infrastructure as the Foundation for Data Warehousing 🔒

08

Role of Metadata in Data Warehousing 🔒

09

Principles of Dimensional Modeling

10

Advanced Topics in Dimensional Modeling 🔒

11

Data Extraction,Transformation and Loading 🔒

12

Importance of Data Quality

13

Matching Information to the Classes of Users

14

OLAP in the Data Warehouse 🔒

15

Data Warehousing and the Web 🔒

16

Data Mining Basics 🔒

17

The Physical Design Process 🔒

18

Data Warehouse Deployment 🔒

19

Growth and Maintenance

20

Important Terms and Definitions

Q11.

How has the size of data warehouses evolved over time?

Data warehouses have become smaller and more focused.

Data warehouses have maintained a consistent size.

Data warehouses have expanded to store larger and larger volumes of data, reaching the terabyte range.

Data warehouses have become obsolete.

Discuss

Answer: (c).Data warehouses have expanded to store larger and larger volumes of data, reaching the terabyte range. Explanation:Data warehouses have expanded to store larger and larger volumes of data, reaching the terabyte range.

Q12.

What is the size of data warehouses in industries such as retail and telecommunications?

Kilobytes

Megabytes

Gigabytes

Terabytes

Discuss

Answer: (d).Terabytes Explanation:Data warehouses in industries such as retail and telecommunications tend to be very large, storing data in the terabyte range.

Q13.

What was the initial state of the data warehousing market regarding vendors and products?

High consolidation and a limited number of vendors

A large number of vendors and products, leading to confusion

A lack of interest from vendors

A well-defined set of data warehousing tools

Discuss

Answer: (b).A large number of vendors and products, leading to confusion Explanation:In the beginning, the data warehousing market was filled with confusion and a large number of vendors and products, leading to vendor hype.

Q14.

How has the data warehousing market evolved over the past decade?

It has remained stagnant and unchanged.

It has become more confusing and unstable.

It has reached maturity, producing off-the-shelf packages and becoming stable.

It has expanded rapidly with new entrants.

Discuss

Answer: (c).It has reached maturity, producing off-the-shelf packages and becoming stable. Explanation:Over the past decade, the data warehousing market has reached maturity, producing off-the-shelf packages and becoming increasingly stable.

Q15.

How are traditional database companies getting involved in the data warehousing market?

They have introduced entirely new database products.

They have remained separate from the data warehousing market.

They have started offering data warehousing solutions built around their existing database products.

They have acquired data warehousing vendors to enter the market.

Discuss

Answer: (c).They have started offering data warehousing solutions built around their existing database products. Explanation:Traditional database companies have begun to offer data warehousing solutions built around their database products.

Q16.

How can the market for data warehousing be broadly classified?

Into strategic data warehouses and tactical data warehouses

Into data warehouses and data marts

Into corporate data warehouses and personal data warehouses

Into integrated data warehouses and fragmented data warehouses

Discuss

Answer: (b).Into data warehouses and data marts Explanation:The market for data warehousing can be broadly classified into data warehouses and data marts.

Q17.

What trend in data warehousing is focused on optimizing queries, indexing large tables, enhancing SQL, improving data compression, and expanding dimensional modeling?

Real-time data warehousing

Multiple data types

Software-driven data warehousing

Tactical decision making

Discuss

Answer: (c).Software-driven data warehousing Explanation:Data warehousing is expected to make big strides in software, especially for optimizing queries, indexing large tables, enhancing SQL, improving data compression, and expanding dimensional modeling.

Q18.

What is a characteristic of real-time data warehousing?

It is focused on historical trends.

It provides information with a significant delay.

It refreshes continuously with almost zero latency.

It is primarily used for strategic decision making.

Discuss

Answer: (c).It refreshes continuously with almost zero latency. Explanation:Real-time data warehousing is described as dynamic, providing the most up-to-date view of the business in real time and continuously refreshing with almost zero latency.

Q19.

What is the trend in data warehousing that involves the integration of both structured and unstructured data types in the data warehouse?

Real-time data warehousing

Software-driven data warehousing

Multiple data types

Tactical decision making

Discuss

Answer: (c).Multiple data types Explanation:The need to integrate both structured and unstructured data types in data warehousing, making it the trend known as "Multiple Data Types."

Q20.

What type of data is considered unstructured data?

Numeric data

Images, text, audio, and video

Structured data

Historical data

Discuss

Answer: (b).Images, text, audio, and video Explanation:Unstructured data includes images, text, audio, and video, among other types.

Page 2 of 12

Building Blocks of Data Warehouse 🔒

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 🔒

The Physical Design Process 🔒

Data Warehouse Deployment 🔒

Welcome to the Trends in Data Warehousing MCQs Page

Trends in Data Warehousing MCQs | Page 2 of 12

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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