Welcome to the Infrastructure as the Foundation for Data Warehousing MCQs Page

Dive deep into the fascinating world of Infrastructure as the Foundation for Data Warehousing with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Infrastructure as the Foundation for 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 Infrastructure as the Foundation for 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 Infrastructure as the Foundation for 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 Infrastructure as the Foundation for Data Warehousing. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Infrastructure as the Foundation for Data Warehousing MCQs | Page 7 of 13

Explore more Topics under Data Warehousing and OLAP

01

The Compelling Need for Data Warehousing

02

Building Blocks of Data Warehouse 🔒

03

Trends in Data Warehousing 🔒

04

Planning and Project Management 🔒

05

Defining the Business Requirements

06

Requirements as the Driving Force for Data warehousing 🔒

07

Architectural Components 🔒

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

Q61.

What is one limitation of the clusters architecture?

High availability

Low operating system overhead

Scalability limited by bus bandwidth

Shared memory among nodes

Discuss

Answer: (c).Scalability limited by bus bandwidth Explanation:One limitation of the clusters architecture is scalability limited by bus bandwidth.

Q62.

Which architecture is recommended for a data warehouse expected to grow in well-defined increments?

Clusters

SMP (Symmetric Multiprocessing)

MPP (Massively Parallel Processing)

NUMA (Non-Uniform Memory Access)

Discuss

Answer: (a).Clusters Explanation:Clusters architecture is recommended for data warehouses expected to grow in well-defined increments.

Q63.

Which of the following architecture can be described as a shared-nothing architecture that is more concerned with disk access than memory access?

Clusters

SMP (Symmetric Multiprocessing)

MPP (Massively Parallel Processing)

NUMA (Cache-Coherent Nonuniform Memory Architecture)

Discuss

Answer: (c).MPP (Massively Parallel Processing) Explanation:The described architecture is MPP (Massively Parallel Processing).

Q64.

Which benefit is associated with the MPP (Massively Parallel Processing) architecture?

Highly scalable

Restricted data access

Low cost per node

Shared memory among nodes

Discuss

Answer: (a).Highly scalable Explanation:One benefit of the MPP architecture is that it is highly scalable.

Q65.

Which architecture is the newest and developed in the 1990s, breaking a big SMP into smaller SMPs that are easier to build?

Clusters

SMP (Symmetric Multiprocessing)

MPP (Massively Parallel Processing)

NUMA (Cache-Coherent Nonuniform Memory Architecture)

Discuss

Answer: (d).NUMA (Cache-Coherent Nonuniform Memory Architecture) Explanation:The described architecture is NUMA (Cache-Coherent Nonuniform Memory Architecture).

Q66.

What does NUMA stand for in NUMA architecture?

National Unified Memory Access

Nonuniform Memory Access

Networked Unified Memory Access

New Universal Memory Architecture

Discuss

Answer: (b).Nonuniform Memory Access Explanation:NUMA stands for Nonuniform Memory Access in NUMA architecture.

Q67.

What benefit is associated with NUMA architecture?

Low software support

Simplified programming

Maximum flexibility

Limited scalability

Discuss

Answer: (c).Maximum flexibility Explanation:One benefit of NUMA architecture is that it provides maximum flexibility.

Q68.

Which option can be described as a more aggressive approach, involving a NUMA machine consisting of one or two SMP nodes?

MPP (Massively Parallel Processing)

Clusters

SMP (Symmetric Multiprocessing)

NUMA (Cache-Coherent Nonuniform Memory Architecture)

Discuss

Answer: (d).NUMA (Cache-Coherent Nonuniform Memory Architecture) Explanation:The described option is NUMA (Cache-Coherent Nonuniform Memory Architecture).

Q69.

Which type of indexing can be very effective in a data warehouse environment when indexing on fields with a smaller number of distinct values?

B-tree indexing

Hash indexing

Bit-mapped indexing

Clustered indexing

Discuss

Answer: (c).Bit-mapped indexing Explanation:Bit-mapped indexing can be effective in indexing fields with a smaller number of distinct values, such as geographic regions in a data warehouse.

Q70.

Which database software feature has been enhanced to assist in bulk refreshes and incremental loading of the data warehouse?

Bit-mapped indexing

Parallel processing

Replication

Aggregation

Discuss

Answer: (c).Replication Explanation:Replication features have been reinforced to assist in bulk refreshes and incremental loading of the data warehouse.

Page 7 of 13

Building Blocks of Data Warehouse 🔒

Trends in Data Warehousing 🔒

Planning and Project Management 🔒

Requirements as the Driving Force for Data warehousing 🔒

Architectural Components 🔒

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 Infrastructure as the Foundation for Data Warehousing MCQs Page

Infrastructure as the Foundation for Data Warehousing MCQs | Page 7 of 13

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

Suggested Topics

Cryptography and Network Security

Hadoop

MongoDB

Neural Networks

Microprocessor

DBMS

Digital Image Processing (DIP)

IT Fundamentals

Artificial Intelligence