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Welcome to the Importance of Data Quality MCQs Page

Dive deep into the fascinating world of Importance of Data Quality with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Importance of Data Quality, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Importance of Data Quality, 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.

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

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Importance of Data Quality MCQs | Page 5 of 10

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Q41.
What integration issues arise in an auction company when the same customer is both a buyer and a seller?

a.

No integration is necessary

b.

Data integration issues are minimal

c.

Integration problems are non-existent

d.

Integration is required to link the buyer and seller data

Discuss
Answer: (d).Integration is required to link the buyer and seller data Explanation:Integration is required to link the data of a customer who is both a buyer and a seller in an auction company.
Q42.
Why do integration problems often arise in legacy systems?

a.

Legacy systems are well-documented and consistent.

b.

Legacy systems are developed in isolation at different times.

c.

Legacy systems use modern and standardized codes.

d.

Legacy systems follow strict data quality rules.

Discuss
Answer: (b).Legacy systems are developed in isolation at different times. Explanation:Integration problems often arise in legacy systems because these systems were developed in isolation at different times, leading to data inconsistencies.
Q43.
What is the primary goal of data cleansing for a data warehouse?

a.

Eliminating data from the data warehouse

b.

Striving to have polluted data

c.

Ensuring data pollution outside the data warehouse

d.

Achieving clean data in the data warehouse

Discuss
Answer: (d).Achieving clean data in the data warehouse Explanation:The primary goal of data cleansing for a data warehouse is to achieve clean data within the data warehouse.
Q44.
Where does data pollution typically occur in the context of a data warehouse?

a.

Inside the data warehouse

b.

Within the control of the data warehouse project team

c.

In old legacy systems outside the data warehouse

d.

In new, modern systems integrated with the data warehouse

Discuss
Answer: (c).In old legacy systems outside the data warehouse Explanation:Data pollution typically occurs in old legacy systems outside the data warehouse.
Q45.
Why is it challenging to address data pollution problems from old operational systems?

a.

Old operational systems have well-documented data

b.

The data warehouse project team has full control over old operational systems

c.

Detailed metadata exists for old legacy systems

d.

The data warehouse project team lacks adequate information about the data in old operational systems

Discuss
Answer: (d).The data warehouse project team lacks adequate information about the data in old operational systems Explanation:It's challenging to address data pollution problems from old operational systems because the data warehouse project team often lacks adequate information about the data in these systems.
Q46.
How does data aging affect data quality in source systems?

a.

It improves data quality

b.

It introduces new data

c.

It enhances data consistency

d.

It may lead to decayed or less meaningful data

Discuss
Answer: (d).It may lead to decayed or less meaningful data Explanation:Data aging may lead to decayed or less meaningful data in source systems.
Q47.
What is a potential issue related to heterogeneous system integration in the context of data quality?

a.

It always improves data consistency

b.

It is never a problem for data quality

c.

Data inconsistency is a common problem

d.

It reduces data pollution

Discuss
Answer: (c).Data inconsistency is a common problem Explanation:Heterogeneous system integration can lead to data inconsistency, which is a common problem for data quality.
Q48.
How does a good database design contribute to data quality?

a.

It has no impact on data quality

b.

It improves data pollution

c.

It reduces the introduction of errors

d.

It encourages data pollution

Discuss
Answer: (c).It reduces the introduction of errors Explanation:Good database design reduces the introduction of errors, which contributes to data quality.
Q49.
What is the role of entity integrity and referential integrity rules in preventing data pollution?

a.

They increase data pollution

b.

They ensure data inconsistency

c.

They have no impact on data quality

d.

They prevent some kinds of data pollution

Discuss
Answer: (d).They prevent some kinds of data pollution Explanation:Entity integrity and referential integrity rules prevent some kinds of data pollution by ensuring the integrity and consistency of the data.
Q50.
What can be a result of incomplete information at the time of data entry for an entity?

a.

Increased data accuracy

b.

Enhanced data quality

c.

Missing values in some fields

d.

Improved data verification

Discuss
Answer: (c).Missing values in some fields Explanation:Incomplete information at the time of data entry can result in missing values in some fields.
Page 5 of 10

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