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Guaranteed AAIA Questions Answers - New Study AAIA Questions
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ISACA AAIA Exam Syllabus Topics:| Topic | Details | | Topic 1 | - AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
| | Topic 2 | - AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
| | Topic 3 | - Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
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ISACA Advanced in AI Audit Sample Questions (Q26-Q31):NEW QUESTION # 26
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?
- A. Monitoring of AI model developers
- B. AI data set anonymization
- C. AI model configuration testing
- D. Monitoring of AI access logs
Answer: C
Explanation:
Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIA™ Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness.
"Model configuration testing simulates real-world threat conditions to validate model resilience. This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs." While access monitoring (C) and anonymization (A) reduce risks, they don't actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Security and Resilience Testing for AI Models"
NEW QUESTION # 27
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:
- A. identify complex data patterns.
- B. ensure full compliance with regulations.
- C. eliminate the need for human intervention.
- D. significantly reduce data bias.
Answer: A
Explanation:
AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIA™ Study Guide states, "AI-based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually." AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Advantages of AI-Enabled Audit Approaches"
NEW QUESTION # 28
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?
- A. A larger training dataset
- B. A validation process for AI decisions
- C. Regular AI model retraining
- D. Prompt templates
Answer: B
Explanation:
WhenAI outputs are not being reviewed, the primary concern is thatincorrect, biased, or non-compliant decisionsmay go undetected. The best way to address this risk is to implement avalidation process for AI decisions(B), which may include human-in-the-loop checks, sampling-based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance ofsupervision and validation of AI outputs, particularly where decisions have financial, legal, or safety implications.
A larger training dataset (A) does not guarantee correctness or fairness and does not replace oversight.
Regular retraining (C) can be beneficial but can also propagate errors if not validated. Prompt templates (D) are relevant mainly for generative and prompt-based systems, and they do not constitute systematic review of outputs. Therefore, astructured validation processis the most direct and effective control.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions).
ISACA audit guidance on AI decision validation and human oversight.
NEW QUESTION # 29
Which of the following insider threats involving the use of AI would present the GREATEST risk?
- A. Destroying system backups
- B. Launching social engineering attacks
- C. Leaking of system hyperparameters
- D. Exfiltrating sensitive data
Answer: D
Explanation:
The GREATEST insider threat isexfiltrating sensitive data(D). AI systems often contain rich datasets including personal, financial, operational, and proprietary information. If an insider extracts or leaks this data, the result can be severe legal, regulatory, and reputational consequences.
Destroying backups (C) affects availability but not confidentiality. Social engineering attacks (B) are serious but indirect. Hyperparameter leakage (A) exposes model configuration but usually does not endanger sensitive data directly. AAIA stresses thatdata confidentiality risksare the most severe category in AI governance.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Risks; Data Protection and Confidentiality.
NEW QUESTION # 30
For a sales promotion, an AI system sorts customer attributes into several categories by analyzing transaction history. Verifying which of the following would BEST validate the effectiveness of this process?
- A. Sensitive attributes are converted to other data types prior to input.
- B. Stress tests are regularly conducted to maintain consistent AI performance.
- C. The applied methodology adequately reflects business objectives.
- D. Sampling of AI output is conducted to identify unusual decisions.
Answer: C
NEW QUESTION # 31
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