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Big Data Analytics Security and Privacy

Description: This quiz covers the fundamentals of Big Data Analytics Security and Privacy, including data protection, access control, and privacy regulations.
Number of Questions: 15
Created by:
Tags: big data analytics security privacy
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What is the primary goal of Big Data Analytics Security?

  1. To ensure the confidentiality, integrity, and availability of big data.

  2. To improve the performance and efficiency of big data analytics systems.

  3. To reduce the cost of big data storage and processing.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

The primary goal of Big Data Analytics Security is to protect big data from unauthorized access, use, disclosure, disruption, modification, or destruction.

Which of the following is NOT a common type of Big Data Analytics Security threat?

  1. Data breaches

  2. Malware attacks

  3. Denial-of-service attacks

  4. Data manipulation

Show answer
Correct Option: D
Explanation:

Data manipulation is not typically considered a Big Data Analytics Security threat, as it involves modifying data rather than accessing or stealing it.

What is the purpose of access control in Big Data Analytics Security?

  1. To restrict access to big data based on user roles and permissions.

  2. To encrypt big data at rest and in transit.

  3. To detect and respond to security incidents in real time.

  4. To ensure the integrity and authenticity of big data.

Show answer
Correct Option: A
Explanation:

Access control in Big Data Analytics Security is used to define who can access big data and what they can do with it.

Which of the following is NOT a common type of access control mechanism used in Big Data Analytics Security?

  1. Role-based access control (RBAC)

  2. Attribute-based access control (ABAC)

  3. Discretionary access control (DAC)

  4. Mandatory access control (MAC)

Show answer
Correct Option: C
Explanation:

Discretionary access control (DAC) is not typically used in Big Data Analytics Security, as it allows users to grant access to other users without any central authority.

What is the purpose of data encryption in Big Data Analytics Security?

  1. To protect big data from unauthorized access.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

Data encryption is used in Big Data Analytics Security to protect big data from unauthorized access, both at rest and in transit.

Which of the following is NOT a common type of data encryption algorithm used in Big Data Analytics Security?

  1. Advanced Encryption Standard (AES)

  2. Triple DES (3DES)

  3. RSA

  4. Blowfish

Show answer
Correct Option: C
Explanation:

RSA is not typically used for data encryption in Big Data Analytics Security, as it is a public-key encryption algorithm that is computationally expensive.

What is the purpose of data masking in Big Data Analytics Security?

  1. To protect sensitive data from unauthorized access.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

Data masking is used in Big Data Analytics Security to protect sensitive data from unauthorized access by replacing it with fictitious or synthetic data.

Which of the following is NOT a common type of data masking technique used in Big Data Analytics Security?

  1. Tokenization

  2. Encryption

  3. Pseudonymization

  4. Generalization

Show answer
Correct Option: B
Explanation:

Encryption is not typically used as a data masking technique, as it does not alter the underlying data values.

What is the purpose of data provenance in Big Data Analytics Security?

  1. To track the origin and lineage of big data.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

Data provenance is used in Big Data Analytics Security to track the origin and lineage of big data, including its sources, transformations, and manipulations.

Which of the following is NOT a common type of data provenance technique used in Big Data Analytics Security?

  1. Lineage tracking

  2. Watermarking

  3. Fingerprinting

  4. Hashing

Show answer
Correct Option: D
Explanation:

Hashing is not typically used as a data provenance technique, as it does not provide information about the origin and lineage of big data.

What is the purpose of security information and event management (SIEM) in Big Data Analytics Security?

  1. To collect, analyze, and respond to security events in real time.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

SIEM is used in Big Data Analytics Security to collect, analyze, and respond to security events in real time, including security breaches, malware attacks, and unauthorized access attempts.

Which of the following is NOT a common type of SIEM tool used in Big Data Analytics Security?

  1. Splunk

  2. ArcSight

  3. LogRhythm

  4. QRadar

Show answer
Correct Option: D
Explanation:

QRadar is not typically used as a SIEM tool in Big Data Analytics Security, as it is primarily designed for network security monitoring.

What is the purpose of privacy regulations in Big Data Analytics Security?

  1. To protect the privacy of individuals whose data is collected and analyzed.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

Privacy regulations are designed to protect the privacy of individuals whose data is collected and analyzed, including their right to control the use and disclosure of their personal information.

Which of the following is NOT a common type of privacy regulation that impacts Big Data Analytics Security?

  1. General Data Protection Regulation (GDPR)

  2. California Consumer Privacy Act (CCPA)

  3. Health Insurance Portability and Accountability Act (HIPAA)

  4. Payment Card Industry Data Security Standard (PCI DSS)

Show answer
Correct Option: D
Explanation:

PCI DSS is not typically considered a privacy regulation, as it focuses on protecting payment card data rather than the privacy of individuals.

What is the purpose of data minimization in Big Data Analytics Security?

  1. To collect and store only the data that is necessary for a specific purpose.

  2. To ensure the integrity and authenticity of big data.

  3. To improve the performance and efficiency of big data analytics systems.

  4. To facilitate the sharing and collaboration of big data among different stakeholders.

Show answer
Correct Option: A
Explanation:

Data minimization is the practice of collecting and storing only the data that is necessary for a specific purpose, in order to reduce the risk of data breaches and unauthorized access.

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