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Questions and Answers
Which of the following is true about data integrity?
Which of the following is true about data integrity?
- Data integrity is a prerequisite for data validation.
- Data integrity refers to the assurance of data accuracy and consistency. (correct)
- Data integrity is the same as data corruption.
- Data integrity is the opposite of data security.
What is the overall intent of any data integrity technique?
What is the overall intent of any data integrity technique?
- To validate data accuracy and consistency.
- To prevent data corruption and loss.
- To ensure data security and protect against unauthorized access.
- To ensure data is recorded exactly as intended and remains the same upon retrieval. (correct)
What is the relationship between data integrity and data quality?
What is the relationship between data integrity and data quality?
- Data integrity ensures data security.
- Data integrity is sometimes used as a proxy term for data quality. (correct)
- Data integrity and data quality are unrelated concepts.
- Data quality is a prerequisite for data integrity.
What is data validation a prerequisite for?
What is data validation a prerequisite for?
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What is data integrity not to be confused with?
What is data integrity not to be confused with?
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Study Notes
Data Integrity Overview
- Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle.
- Techniques ensuring data integrity aim to maintain and protect data from unauthorized access, corruption, or loss.
Overall Intent of Data Integrity Techniques
- The primary purpose of data integrity techniques is to ensure that data remains unaltered and authentic during processing and storage.
- Methods include validation, error checking, and audit trails to uphold data preservation.
Relationship Between Data Integrity and Data Quality
- High data integrity directly enhances data quality, indicating trustworthy and precise information.
- Data quality encompasses completeness, accuracy, and relevance; thus, integrity acts as a foundational component of quality assessments.
Prerequisites of Data Validation
- Data validation is essential for maintaining data integrity, ensuring that the data meets predefined standards before entry into a system.
- It helps in identifying errors and inconsistencies, preventing them from impacting downstream applications.
Misconceptions About Data Integrity
- Data integrity should not be confused with data security, which deals with protecting data from unauthorized access and breaches.
- While security focuses on safeguarding data, integrity emphasizes maintaining its original state and reliability.
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