Welcome to the OLAP in the Data Warehouse MCQs Page

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

OLAP in the Data Warehouse MCQs | Page 4 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

Infrastructure as the Foundation for Data Warehousing 🔒

09

Role of Metadata in Data Warehousing 🔒

10

Principles of Dimensional Modeling

11

Advanced Topics in Dimensional Modeling 🔒

12

Data Extraction,Transformation and Loading 🔒

13

Importance of Data Quality

14

Matching Information to the Classes of Users

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

Q31.

What does the guideline for "Drill-Through to Detail Level" allow in an OLAP system?

Smooth transition from aggregated to preaggregated data

Access to external data serving as the source at the detail record level

Restriction on accessing detail records

Limited analysis models

Discuss

Answer: (b).Access to external data serving as the source at the detail record level Explanation:The guideline for "Drill-Through to Detail Level" allows a smooth transition from the multidimensional, preaggregated database to the detail record level of the source data warehouse repository.

Q32.

What analysis models does Dr. Codd's guidelines support?

Only exegetical and categorical

Only contemplative and formulaic

All four: exegetical, categorical, contemplative, and formulaic

None of the above

Discuss

Answer: (c).All four: exegetical, categorical, contemplative, and formulaic Explanation:Dr. Codd's guidelines support all four analysis models: exegetical (or descriptive), categorical (or explanatory), contemplative, and formulaic.

Q33.

What should be the treatment of nonnormalized data in an OLAP system?

Allow calculations to affect external data

Prohibit calculations from affecting the external data

Only perform calculations within the OLAP system

Ignore missing values

Discuss

Answer: (b).Prohibit calculations from affecting the external data Explanation:The treatment of nonnormalized data in an OLAP system is to prohibit calculations made within the system from affecting the external data serving as the source.

Q34.

What is the recommendation for deploying OLAP tools on transactional systems?

Use write-capable OLAP tools

Avoid deploying OLAP tools on top of transactional systems

Use OLAP tools only for reporting

Limit OLAP tools to specific departments

Discuss

Answer: (b).Avoid deploying OLAP tools on top of transactional systems Explanation:OLAP tools should not be deployed on top of transactional systems; instead, write-capable OLAP tools should be avoided.

Q35.

What should be done with missing values in an OLAP system?

Address missing values in calculations

Replace missing values with default values

Ignore missing values, irrespective of their source

Prompt users to enter missing values

Discuss

Answer: (c).Ignore missing values, irrespective of their source Explanation:In an OLAP system, missing values should be ignored, irrespective of their source.

Q36.

What is the requirement for incremental refreshes in an OLAP system?

Periodic full refreshes

Continuous full refreshes

Incremental refreshes of extracted and aggregated OLAP data

No refreshes are needed

Discuss

Answer: (c).Incremental refreshes of extracted and aggregated OLAP data Explanation:An OLAP system should provide for incremental refreshes of the extracted and aggregated OLAP data.

Q37.

How should the SQL interface be integrated into an OLAP system?

Separately from the enterprise environment

Seamlessly integrate the OLAP system into the existing enterprise environment

Only allow OLAP tools to use the SQL interface

Limit access to the SQL interface

Discuss

Answer: (b).Seamlessly integrate the OLAP system into the existing enterprise environment Explanation:The SQL interface should be seamlessly integrated into the existing enterprise environment in an OLAP system.

Q38.

What is one of the fundamental characteristics of OLAP systems?

Providing simple access to business intelligence

Offering a one-dimensional view of data

Allowing business users a multidimensional and logical view of data

Restricting interactive query options

Discuss

Answer: (c).Allowing business users a multidimensional and logical view of data Explanation:One of the fundamental characteristics of OLAP systems is allowing business users a multidimensional and logical view of the data in the data warehouse.

Q39.

What is the purpose of OLAP systems in relation to data warehousing?

OLAP systems replace data warehousing

OLAP systems complement data warehousing by providing a new interface

OLAP systems restrict access to business intelligence

OLAP systems simplify data warehousing

Discuss

Answer: (b).OLAP systems complement data warehousing by providing a new interface Explanation:OLAP systems complement data warehousing by lifting the information delivery capabilities to new heights.

Q40.

What is the role of OLAP in information delivery?

OLAP is an alternative to data warehousing

OLAP is a layer in the data warehouse

OLAP is only an interface between data and users

OLAP is an information delivery system that complements the data warehouse

Discuss

Answer: (d).OLAP is an information delivery system that complements the data warehouse Explanation:OLAP is an information delivery system that complements the data warehouse.

Page 4 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 🔒

Infrastructure as the Foundation for Data Warehousing 🔒

Role of Metadata in Data Warehousing 🔒

Advanced Topics in Dimensional Modeling 🔒

Data Extraction,Transformation and Loading 🔒

Data Warehousing and the Web 🔒

Data Mining Basics 🔒

The Physical Design Process 🔒

Data Warehouse Deployment 🔒

Welcome to the OLAP in the Data Warehouse MCQs Page

OLAP in the Data Warehouse MCQs | Page 4 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

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