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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operations | 46% | - Operational Controls and Readiness - Incident Management and Resilience - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment |
| Topic 2: AI Auditing Tools and Techniques | 21% | - Audit Testing and Sampling Methodologies - Audit Planning and Design - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Data Quality and Analytics for AI Audit |
| Topic 3: AI Governance and Risk | 33% | - AI Governance and Program Management - AI Risk Management - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - Ethics, Regulations, and Standards for AI |
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NEW QUESTION # 303
What is the MOST significant audit risk when an organization uses a pretrained foundation model fine-tuned on internal data?
Answer: D
Explanation:
Fine-tuning does not eliminate biases or weaknesses baked into the base model; if the foundation model's original training data and behavior are not transparent, inherited risks can persist undetected.
NEW QUESTION # 304
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?
Answer: B
Explanation:
In ahigh-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. TheGREATEST concernfor an IS auditor is when the processfails to identify anomalies that can bias training data(A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.
Option B (lack of regular quality reviews) is serious but is partially addressed if anomaly detection is effective. Option C (infrequent updates) may degrade detection performance over time but is less critical than outright failure to detect harmful anomalies. Option D (staff training) is important for operational effectiveness but still secondary to the technical failure to catch bias-inducing anomalies. AAIA stresses that data integrity and monitoring controlsare paramount in high-risk contexts.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions, data monitoring, and anomaly detection).
ISACA AI risk materials focusing on high-risk AI oversight and data integrity.
NEW QUESTION # 305
Which of the following is the GREATEST risk when a generative AI tool used for threat detection produces inaccurate or misleading information?
Answer: A
Explanation:
In the context of cybersecurity and threat detection, the "accuracy" of AI outputs is a matter of organizational safety. The greatest risk of inaccurate information (such as false negatives) is that
"Potential threats may be overlooked," leading to undetected breaches or system compromises.
This occurs when the model fails to flag a genuine anomaly as suspicious.
NEW QUESTION # 306
Which of the following is the BEST recommendation for an organization that has adopted " vibe coding " (using AI to generate code based on high-level natural language prompts)?
Answer: C
Explanation:
" Vibe coding " or AI-assisted development carries the risk of introducing " hallucinated " vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led " security checklist " for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials. Prompting the AI to check its own code (Option C) is unreliable because the model may suffer from the same biases or logic errors that created the code in the first place.
NEW QUESTION # 307
Which of the following is the MOST important purpose of conducting a risk assessment for AI models within an organization?
Answer: B
Explanation:
Risk assessments identify potential threats and vulnerabilities in AI systems and support the development of mitigation strategies. According to the AAIATM 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."
NEW QUESTION # 308
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