Google Cloud SQL - Data masking and redaction MCQ

Google Cloud SQL – 27 – Data masking and redaction MCQ

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1. What is the primary purpose of data masking and redaction in Google Cloud SQL?

Ensuring data accuracy

Protecting sensitive data

Enhancing query performance

Reducing storage costs

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2. What is the key difference between data masking and data redaction?

Data masking is used for data at rest, while data redaction is used for data in transit.

Data masking conceals sensitive data, while data redaction replaces it with predefined placeholders.

Data masking is irreversible, while data redaction is reversible.

Data masking uses encryption, while data redaction uses pseudonyms.

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3. Why are data masking and redaction crucial in Google Cloud SQL?

To simplify data validation

To reduce data storage costs

To improve query performance

To protect sensitive information and comply with data privacy regulations

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4. What is the primary benefit of data redaction over data masking?

Data redaction is more efficient for query performance.

Data redaction replaces sensitive data with predefined placeholders.

Data redaction is suitable for data at rest only.

Data redaction is irreversible.

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5. Which regulations may require organizations to implement data masking and redaction?

Sarbanes-Oxley Act (SOX)

PCI DSS and ISO 27001

COPPA and FERPA

GDPR, HIPAA, and CCPA

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6. What is the first step in implementing data masking and redaction in Google Cloud SQL?

Identifying sensitive data elements

Implementing strong access controls

Testing SQL queries

Defining data masking and redaction policies

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7. What does data redaction involve?

Storing sensitive data in plain text

Irreversibly removing sensitive data

Encrypting sensitive data

Replacing sensitive data with predefined placeholders

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8. What is the primary purpose of data masking in Google Cloud SQL?

To simplify data validation

To protect sensitive data

To lower data storage costs

To reduce query performance

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9. Which step in implementing data masking and redaction involves specifying how sensitive data should be transformed or replaced?

Implementing data masking/Redaction in SQL queries

Data classification

Data masking and redaction policies

Monitoring and auditing

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10. What should organizations do to maintain consistent data masking and redaction policies?

Encrypt all data at rest

Regularly test and validate policies

Store sensitive data in plain text

Implement strong access controls

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11. Which aspect of data security do data masking and redaction primarily address?

Data at rest encryption

Data availability

Data confidentiality

Data in transit encryption

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12. Why is it important to enforce strict access controls when using data masking and redaction?

To prevent unauthorized access to sensitive data

To enhance query performance

To simplify data validation

To reduce storage costs

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13. What is the primary use case for data masking and redaction in non-production environments?

To enforce strict schema rules

To protect sensitive data during testing and development

To improve query performance

To reduce data storage costs

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14. What should organizations consider when implementing data masking and redaction for compliance reporting?

The need for strong access controls

Irreversible redaction methods

Testing and validation before implementation

Ensuring data accuracy

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15. What is the significance of auditing and monitoring when implementing data masking and redaction?

To reduce data storage costs

To simplify data validation

To track access to sensitive data and demonstrate compliance

To optimize query performance

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16. Which technique replaces sensitive data with predefined patterns in data redaction?

Data tokenization

Data pseudonymization

Data encryption

Data obfuscation

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17. What should organizations do to minimize risk exposure when using data masking and redaction?

Store all data in plain text

Encrypt all data in transit

Minimize the storage of sensitive data

Regularly update masking and redaction policies

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18. In data redaction, what is typically used to replace sensitive data elements?

Asterisks (*)

Data hashes

Pseudonymous values

Encryption keys

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19. Which technique ensures that reports and analytics generated from the database do not expose sensitive data?

Data obfuscation

Data masking and redaction

Data encryption

Data compression

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20. What is the primary goal of data masking and redaction when sharing data with third parties?

To ensure data accuracy

To improve query performance

To lower data storage costs

To protect sensitive information

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21. How can organizations ensure that sensitive data remains protected even when temporarily exposed in its masked or redacted form?

By avoiding data masking and redaction altogether

By using reversible data redaction methods

By implementing strong access controls and authentication mechanisms

By disabling data masking and redaction

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22. Which technique replaces sensitive data with pseudonymous values in data masking?

Data pseudonymization

Data obfuscation

Data tokenization

Data hashing

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23. What is one key advantage of data redaction for compliance with data privacy regulations?

It allows reversible transformation of sensitive data.

It enhances query performance.

It makes data permanently inaccessible.

It replaces sensitive data with predefined placeholders.

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24. Which of the following is NOT a best practice for data masking and redaction in Google Cloud SQL?

Maintaining inconsistent policies

Regularly testing and validating policies

Minimizing the storage of sensitive data

Implementing strong access controls

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25. What is the primary focus of data masking and redaction in Google Cloud SQL?

Improving query performance

Safeguarding sensitive data

Protecting data during transmission

Reducing storage costs

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Google Cloud SQL – Data masking and redaction MCQ