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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.

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

Importance of Data Quality MCQs | Page 6 of 10

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Q51.
How does the entry of generic values into mandatory fields affect data quality?

a.

It improves data quality

b.

It ensures data accuracy

c.

It prevents data pollution

d.

It can lead to data pollution

Discuss
Answer: (d).It can lead to data pollution Explanation:The entry of generic values into mandatory fields can lead to data pollution.
Q52.
What role does data verification play in preventing data corruption?

a.

Data verification ensures data corruption

b.

Data verification is the primary source of data corruption

c.

Data verification is not related to data quality

d.

Data verification can help ensure data accuracy and prevent data corruption

Discuss
Answer: (d).Data verification can help ensure data accuracy and prevent data corruption Explanation:Data verification can help ensure data accuracy and prevent data corruption.
Q53.
In a data warehouse, why is deduplication of names and addresses essential?

a.

To create more records for a single person

b.

To increase the number of duplicate records

c.

To consolidate the activities of each person from duplicate records

d.

To maintain data in its original form

Discuss
Answer: (c).To consolidate the activities of each person from duplicate records Explanation:Deduplication of names and addresses is essential in a data warehouse to consolidate the activities of each person from duplicate records.
Q54.
What are some inherent problems with entering names and addresses in the multiple field format?

a.

Unique key for every record

b.

Single names on each line

c.

Multiple addresses for the same person

d.

Many names on one line

Discuss
Answer: (d).Many names on one line Explanation:Inherent problems with entering names and addresses in the multiple field format include having many names on one line.
Q55.
What is a key challenge in recasting name and address data into the multiple field format?

a.

Matching customer records

b.

Finding duplicate records

c.

Devising matching algorithms

d.

Dealing with variations in data entry

Discuss
Answer: (d).Dealing with variations in data entry Explanation:A key challenge in recasting name and address data into the multiple field format is dealing with variations in data entry.
Q56.
How can costs related to bad decisions based on routine analysis be estimated?

a.

By forecasting lost opportunities

b.

By implementing redundant data

c.

By using corrupt data

d.

By increasing the number of source systems

Discuss
Answer: (a).By forecasting lost opportunities Explanation:Costs related to bad decisions based on routine analysis can be estimated by forecasting lost opportunities.
Q57.
Data cleansing tools primarily serve which two main functions for improving data quality?

a.

Data validation and data consolidation

b.

Data error discovery and data correction

c.

Data extraction and data transformation

d.

Data cleansing and data enrichment

Discuss
Answer: (b).Data error discovery and data correction Explanation:Data cleansing tools primarily serve the functions of data error discovery and data correction for improving data quality.
Q58.
What are some features of error discovery functions in data cleansing tools?

a.

Group and relate customer records

b.

Standardize data elements to common formats

c.

Detect inconsistencies among data items from different sources

d.

Improve merging of data from dissimilar data sources

Discuss
Answer: (c).Detect inconsistencies among data items from different sources Explanation:Some features of error discovery functions in data cleansing tools include detecting inconsistencies among data items from different sources.
Q59.
What are some features of data correction functions in data cleansing tools?

a.

Identify duplicate records

b.

Validate for allowable values

c.

Normalize inconsistent data

d.

Monitor trends in data quality over time

Discuss
Answer: (c).Normalize inconsistent data Explanation:Some features of data correction functions in data cleansing tools include normalizing inconsistent data.
Q60.
How can data cleansing tools assist in the reconciliation of problems related to RDBMS referential integrity?

a.

By standardizing data elements

b.

By grouping and relating customer records

c.

By improving merging of data from dissimilar data sources

d.

By detecting inconsistencies among data items from different sources

Discuss
Answer: (c).By improving merging of data from dissimilar data sources Explanation:Data cleansing tools can assist in the reconciliation of problems related to RDBMS referential integrity by improving merging of data from dissimilar data sources.
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