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[Hardware] Released ISACA AAIA Questions Tips For Better Preparation [2026]

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【Hardware】 Released ISACA AAIA Questions Tips For Better Preparation [2026]

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ISACA AAIA Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q169-Q174):NEW QUESTION # 169
An IS auditor is considering using a web-based AI tool to update an audit report. What should be the MOST important consideration before inputting the report?
  • A. Safeguard measures of the AI tool
  • B. Compliance with organizational data protection requirements
  • C. Impact on the audit budget
  • D. The AI tool alignment with organizational report formatting
Answer: B
Explanation:
The MOST important factor iscompliance with organizational data protection requirements(D), because audit reports contain sensitive internal information, findings, and potential control deficiencies. Sending such content to an external AI tool may result in data exposure or unauthorized processing. AAIA emphasizes privacy, confidentiality, and data protection obligationswhen interacting with external AI systems.
Option B (safeguards) is important but still secondary to ensuring compliance with internal data protection rules. Formatting alignment (A) and budget considerations (C) do not address the primary risk: improper disclosure of confidential audit data. Therefore,data protection complianceis the priority.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Legal, Privacy, and Ethical Considerations in AI.

NEW QUESTION # 170
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?
  • A. Monitoring of AI access logs
  • B. AI data set anonymization
  • C. AI model configuration testing
  • D. Monitoring of AI model developers
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 # 171
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
  • A. The effectiveness of data anonymization processes that help preserve data quality
  • B. The measures in place to ensure the appropriateness and relevance of input data for generative AI models
  • C. The algorithms for generative AI models designed to detect and correct data bias before processing
  • D. The frequency of data audits verifying the integrity and accuracy of inputs
Answer: B
Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations-where generative models produce fabricated or misleading outputs. The AAIA™ Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI"

NEW QUESTION # 172
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?
  • A. Require manual reviews of all AI-generated recruitment decisions before hiring is finalized.
  • B. Suspend the use of the AI system until the training data can be verified for fairness and compliance.
  • C. Retrain the AI model using an external data set certified for inclusivity and fairness.
  • D. Implement a process to periodically test the AI system for biases and adjust parameters as needed.
Answer: D
Explanation:
Periodic testing and monitoring for bias is a sustainable, proactive strategy aligned with best practices outlined in the AAIA™ Study Guide. This approach ensures that the AI system remains compliant over time, even as data and hiring conditions change.
"Ongoing fairness assessments help detect emerging biases and ensure that the AI model maintains equitable decision-making standards. Periodic testing also allows organizations to take corrective action before regulatory or reputational damage occurs." Suspending the system (B) or relying solely on external datasets (C) are temporary or limited in scope.
Manual reviews (D) are effective but do not solve the root issue. Therefore, A provides a comprehensive, audit-aligned solution.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias Mitigation and Monitoring"

NEW QUESTION # 173
An IS auditor examining change management procedures for an AI system observes inconsistent training data validation and verification protocols prior to model retraining. Which of the following is the MOST significant risk in this context?
  • A. Delays in AI model retraining due to procedural inefficiencies
  • B. Noncompliance due to inadequate model training documentation
  • C. Degradation of system reliability due to compromised or substandard data
  • D. Addition of AI model complexity due to inconsistent data inputs
Answer: C
Explanation:
When training data validation is inconsistent, the most severe risk is that the AI model may learn from incorrect, incomplete, biased, or corrupted data. This directly leads to a degradation of system reliability (option C), which manifests as inaccurate predictions, higher error rates, bias, or unstable behavior.
AAIA emphasizes that data validation prior to retraining is one of the most important controls because model behavior is fully dependent on training data integrity. If the quality and correctness of the data cannot be guaranteed, the resulting model outputs become unreliable, which can undermine compliance, operational decisions, and user trust.
Option A is less critical because increased complexity is not the core risk. Option B is important but secondary; documentation issues do not inherently degrade model reliability. Option D is an efficiency issue, not a risk to output integrity.
Therefore, compromised reliability due to poor-quality training data is the most significant risk.
References:
AAIA Domain 2: Data Management Specific to AI (data validation, verification, data quality).
AAIA Domain 1: Governance and Risk Controls for AI.

NEW QUESTION # 174
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