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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q114-Q119):

NEW QUESTION # 114
An IS auditor is interviewing management about implemented controls around machine learning (ML) models deployed in the production environment. Which of the following schedules for reviewing the performance of a deployed model would be of GREATEST concern to the auditor?

Answer: B

Explanation:
Only reviewing an ML model's performance one time prior to migrating to production (option C) is of greatest concern. The AAIATM Study Guide emphasizes that "AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors." A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.
Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.


NEW QUESTION # 115
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?

Answer: B

Explanation:
Images from social media platforms (C) pose the greatest risk to data integrity because:
Images may be heavily filtered or edited
Lighting, scale, and resolution are inconsistent
Metadata is unreliable
Medical diagnosis labels (if any) are unverified
Context is unknown and may include misinformation
Sources cannot be validated or authenticated
AAIA stresses that medical AI systems require high-quality, clinically validated datasets with clear provenance, accuracy, and consent.


NEW QUESTION # 116
What is the MOST significant audit risk when an organization uses a pretrained foundation model fine-tuned on internal data?

Answer: C

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 # 117
During a walkthrough, an auditor learns that model retraining occurs automatically without any approval gate. What is the GREATEST risk this presents?

Answer: D

Explanation:
Automated retraining without a human approval/validation gate can silently push a degraded or biased model into production, bypassing change management controls.


NEW QUESTION # 118
The PRIMARY benefit of using AI in audit fieldwork is that it:

Answer: A

Explanation:
According to the AAIATM manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk- based approach.


NEW QUESTION # 119
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