Answer-first summary for fast verification
Answer: Incorporating all of the above components to create a holistic data governance framework that addresses data quality, security, and compliance in a unified manner.
Option D is the correct answer because a holistic approach is necessary to address the multifaceted requirements of data governance in a complex, multinational data warehouse environment. Data quality monitoring and validation processes (Option A) ensure the reliability of data for decision-making. Data security policies and controls (Option B) are critical for protecting sensitive information against breaches and ensuring privacy. Data lineage tracking and audit capabilities (Option C) are essential for regulatory compliance and understanding data flow and transformations. Together, these components form a comprehensive framework that meets the corporation's needs for data quality, security, and compliance.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
As a Microsoft Fabric Analytics Engineer Associate, you are designing a comprehensive data governance framework for a multinational corporation's data warehouse environment. The framework must ensure high data quality, robust security measures, and strict compliance with international regulatory requirements such as GDPR and CCPA. Given the complexity of the environment, which includes multiple data sources and varying levels of data sensitivity, which of the following components would you prioritize in your framework to address these requirements effectively? (Choose the best option)
A
Implementing data quality monitoring and validation processes to ensure the accuracy and completeness of the data, focusing on automated validation rules and anomaly detection.
B
Establishing data security policies and controls, including encryption at rest and in transit, role-based access control (RBAC), and regular security audits to protect sensitive data and prevent unauthorized access.
C
Deploying data lineage tracking and audit capabilities to provide comprehensive visibility into data origins, transformations, and usage, facilitating compliance with regulatory requirements and data impact analysis.
D
Incorporating all of the above components to create a holistic data governance framework that addresses data quality, security, and compliance in a unified manner.