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ISACA Advanced in AI Audit Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which of the following is the PRIMARY objective of AI governance?

Answer: B

Explanation:
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.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Governance Objectives and Structures"


NEW QUESTION # 83
Which of the following is the GREATEST risk associated with using AI in audit planning?

Answer: B

Explanation:
In the context of AI-driven audit planning, the AAIA™ Study Guide identifies incomplete or inaccurate data as the most significant risk. If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.
"Audit planning supported by AI tools is only as strong as the underlying data. Incomplete data may cause incorrect risk assessments or misallocate audit resources, undermining audit effectiveness." While scope creep and cost are common concerns in project management, and limited AI knowledge can be mitigated through training, only incomplete data directly impacts the validity of audit planning outputs.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Data Integrity and Planning Risks"


NEW QUESTION # 84
Which of the following BEST demonstrates effective coordination to ensure comprehensive oversight of an AI system deployed across multiple jurisdictions?

Answer: B

Explanation:
When AI systems spanmultiple jurisdictions, they are subject to different regulatory regimes, cultural expectations, and risk tolerances. AAIA emphasizes that oversight must be coordinated to avoid gaps or overlaps.Establishing joint oversight plans and communication channels between agencies(B) ensures that relevant authorities share information, align on expectations, and collectively monitor AI risks, enabling coherent and comprehensive oversight.
Option A focuses too narrowly on technical metrics without ensuring cross-jurisdiction coordination. Option C may not be feasible or lawful, as jurisdictional sovereignty often prevents centralizing authority. Option D emphasizes automation but does not address governance and coordination. Thus, the best demonstration of effective oversight for cross-jurisdiction AI deployments isformal joint oversight and structured communication.
References:
ISACA,AAIA Exam Content Outline- Governance of AI (roles, responsibilities, coordination among stakeholders).
ISACA materials addressing multi-jurisdictional AI risk, regulatory alignment, and oversight structures.


NEW QUESTION # 85
Which of the following is the PRIMARY advantage of using K-fold cross validation when evaluating the performance of a machine learning (ML) model?

Answer: A

Explanation:
The primary advantage ofK-fold cross validationis that it uses multiple train/test splits, cycling through all folds so that each observation is used both for training and testing at different points. This process provides a more reliable estimate of model performance andreduces the risk of overfitting to a single split(option D).
It is an established best practice in model evaluation and aligns with AAIA's emphasis ontesting techniques for AI solutions and data analytics.
Option A is not specific to regressions; cross validation can be used for classification and other models as well. Option B can actually increase computational cost since multiple models are trained. Option C misunderstands bias-variance trade-offs; increasing K doesn't simply "reduce model bias." The key advantage remains the use of repeated, varied splits to better assess generalization and guard against overfitting.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Testing Techniques for AI Solutions; AI- specific testing).
ISACA data analytics content used in AAIA prep covering cross validation as a standard evaluation method.


NEW QUESTION # 86
The PRIMARY objective of machine learning (ML) in data processing is to:

Answer: B

Explanation:
The AAIA™ Study Guide defines the core purpose of machine learning as the ability to enable systems to learn from data and make decisions or perform tasks that typically require human cognitive functions. ML allows AI systems to identify patterns, learn from historical data, and automate complex decision-making.
"Machine learning empowers systems to simulate aspects of human intelligence, including pattern recognition, language understanding, and decision-making. It forms the backbone of many AI applications designed to replace or augment human tasks." While visual analysis (A) and statistical inference (D) are functions of ML, they are subsets-not primary goals. Explainability (B) is important but is not a core ML function. Thus, C best represents the primary objective.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Machine Learning Basics and Objectives"


NEW QUESTION # 87
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