Isaca CGEIT Actual Questions

Data stewards are primarily responsible for applying frameworks for the governance of IT to balance the need for security controls with business requirements, as they are the custodians of the data quality, integrity, and security within an organization. Data stewards define and implement data policies, standards, and procedures, as well as monitor and report on data compliance and performance. Data stewards also collaborate with other stakeholders, such as data owners, data users, and data scientists, to ensure that the data is aligned with the business objectives and meets the regulatory and ethical requirements.

Data scientists, data analysts, and data processors are not primarily responsible for applying frameworks for the governance of IT, as they are more focused on the technical aspects of data collection, analysis, and processing. Data scientists use advanced methods and tools to extract insights and value from data. Data analysts use descriptive and inferential statistics to explore and interpret data. Data processors perform operations on data, such as entry, validation, transformation, storage, and retrieval.

References := What is a Data Steward? | Informatica; Data Governance Roles: The Ultimate Guide | Collibra; Data Governance Roles: Who Does What? | erwin; [What is IT governance? A formal way to align IT & business strategy].

 The first course of action when assessing the impact of a new regulatory requirement is to map the regulation to business processes. This means identifying and analyzing which business processes are affected by the new regulation, how they are affected, and what changes are needed to comply with the regulation1. Mapping the regulation to business processes helps to understand the scope, complexity, and priority of the regulatory compliance project, and to align the IT and business objectives and strategies1. It also helps to identify the stakeholders, roles, responsibilities, and risks involved in the compliance process, and to communicate and coordinate with them effectively1. The other options are not as important as mapping the regulation to business processes, as they are dependent on the outcome of this step. Updating affected IT policies, assessing the budget impact of the new regulation, and implementing new regulatory requirements are subsequent steps that should be done after mapping the regulation to business processes2. References: How to Map Regulations to Business Processes. CGEIT Certification | Certified in Governance of Enterprise IT | ISACA.

 A data classification policy is a plan that helps an organization determine the risk tolerance and security requirements for its data assets. A data classification policy separates data into different categories based on its sensitivity, such as public, private, or restricted. A data classification policy should be established first so that data owners can consistently assess the level of data protection needed across the enterprise, as it helps them to identify the types and locations of data they own, the potential threats and impacts of data breaches, and the appropriate security controls and measures to safeguard their data. A data classification policy also helps to ensure compliance with regulatory and legal obligations, as well as to optimize data management and governance practices. References: CGEIT Exam Content Outline | ISACA1, CGEIT Review Manual (Digital Version), Data Classification Policy: Benefits, Examples, and Techniques2, Why data classification is important for security | Infosec3

The enterprise data architecture is the blueprint that defines how data is collected, stored, processed, integrated, and distributed within the enterprise. It also specifies the data standards, policies, rules, and models that govern the data lifecycle. Reviewing the enterprise data architecture is the first step when preparing for data migration, as it helps to identify the source and target systems, the data entities and attributes, the relationships and dependencies, the data quality and security requirements, and the potential challenges and risks of the migration. Reviewing the enterprise data architecture also helps to align the data migration with the business objectives and strategies, and to ensure that the migrated data supports the enterprise’s information needs and decision making. References: CGEIT Exam Content Outline | ISACA1, CGEIT Review Manual (Digital Version), Data Architecture: A Primer for the Data Scientist, Data Migration: A Comprehensive Guide