Isaca AAIA Dumps

Imputing missing values using the mean, median, or mode is a common technique to fill blanks in datasets. However, according to the AAIA™ Study Guide, this method introduces a significant risk of information distortion, especially if the data is not normally distributed or if imputed values disproportionately impact underrepresented groups.

“Simple imputation can reduce data variability and reinforce existing biases. It may also misrepresent the true distribution of the data, leading to skewed model outputs.”

Other options (B, C, D) may involve transformations, but they do not inherently cause as much bias or data misrepresentation as A.

Risk assessments identify potential threats and vulnerabilities in AI systems and support the development of mitigation strategies. According to the AAIA™ Study Guide, the main objective is not just identification of risks but to proactively design controls that minimize impact and ensure ethical and regulatory compliance.

“The output of an AI risk assessment should lead to actionable mitigation strategies, supporting the secure and responsible deployment of AI solutions across business processes.”

Options A, C, and D are components of governance and monitoring, but B addresses the core intent of risk assessments.

The AAIA™ Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.

“AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment.”

Options A and C are essential components of governance but are not its core definition. Option D is a business outcome, not a governance goal. Thus, B is the most comprehensive and accurate objective.