Big Data Analytics Governance and Compliance

This quiz will test your knowledge on Big Data Analytics Governance and Compliance.

15 Questions Published

Questions

Question 1 Multiple Choice (Single Answer)

What is the primary objective of Big Data Analytics Governance?

  1. To ensure data accuracy and consistency
  2. To improve data accessibility and usability
  3. To establish data security and privacy controls
  4. To optimize data storage and processing efficiency
Question 2 Multiple Choice (Single Answer)

Which framework provides guidance for Big Data Governance?

  1. ISO/IEC 27001
  2. NIST SP 800-53
  3. COBIT 5
  4. GDPR
Question 3 Multiple Choice (Single Answer)

What is the role of data lineage in Big Data Analytics Governance?

  1. To track the movement of data across systems
  2. To identify the source and transformation of data
  3. To ensure data quality and accuracy
  4. To facilitate data discovery and exploration
Question 4 Multiple Choice (Single Answer)

Which regulation imposes strict data protection requirements on organizations in the European Union?

  1. HIPAA
  2. PCI DSS
  3. GDPR
  4. SOX
Question 5 Multiple Choice (Single Answer)

What is the purpose of data retention policies in Big Data Analytics Governance?

  1. To define the criteria for storing and archiving data
  2. To ensure data is accessible for authorized users
  3. To protect data from unauthorized access and modification
  4. To optimize data storage and processing costs
Question 6 Multiple Choice (Single Answer)

Which technology is commonly used for data masking in Big Data environments?

  1. Encryption
  2. Tokenization
  3. Pseudonymization
  4. Data scrambling
Question 7 Multiple Choice (Single Answer)

What is the responsibility of a Chief Data Officer (CDO) in Big Data Analytics Governance?

  1. To oversee data strategy and governance initiatives
  2. To manage data quality and data integration
  3. To develop data analytics solutions and applications
  4. To ensure data security and compliance
Question 8 Multiple Choice (Single Answer)

Which framework provides guidance for data governance in the financial industry?

  1. Basel II
  2. Sarbanes-Oxley Act (SOX)
  3. Payment Card Industry Data Security Standard (PCI DSS)
  4. ISO 27001
Question 9 Multiple Choice (Single Answer)

What is the purpose of data classification in Big Data Analytics Governance?

  1. To identify and categorize data based on its sensitivity and criticality
  2. To ensure data is stored and processed in appropriate systems
  3. To facilitate data access and sharing among authorized users
  4. To optimize data storage and processing costs
Question 10 Multiple Choice (Single Answer)

Which technology is commonly used for data encryption in Big Data environments?

  1. AES
  2. RSA
  3. ECC
  4. SHA-256
Question 11 Multiple Choice (Single Answer)

What is the role of metadata in Big Data Analytics Governance?

  1. To provide information about data assets, such as their structure, format, and lineage
  2. To facilitate data discovery and exploration
  3. To ensure data quality and accuracy
  4. To optimize data storage and processing efficiency
Question 12 Multiple Choice (Single Answer)

Which regulation imposes data privacy requirements on healthcare organizations in the United States?

  1. HIPAA
  2. PCI DSS
  3. GDPR
  4. SOX
Question 13 Multiple Choice (Single Answer)

What is the purpose of data governance councils in Big Data environments?

  1. To provide oversight and guidance on data governance initiatives
  2. To develop and enforce data governance policies and standards
  3. To facilitate collaboration and communication among data stakeholders
  4. To ensure compliance with regulatory requirements
Question 14 Multiple Choice (Single Answer)

Which technology is commonly used for data anonymization in Big Data environments?

  1. Encryption
  2. Tokenization
  3. Pseudonymization
  4. Data scrambling
Question 15 Multiple Choice (Single Answer)

What is the role of data quality management in Big Data Analytics Governance?

  1. To ensure data is accurate, consistent, and complete
  2. To identify and correct data errors and inconsistencies
  3. To establish data quality standards and metrics
  4. To monitor data quality and take corrective actions when necessary