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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 (Q133-Q138):

NEW QUESTION # 133
The PRIMARY objective of machine learning (ML) in data processing is to:

Answer: D


NEW QUESTION # 134
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 # 135
A financial institution ' s customer service chatbot sometimes gives users incorrect legal advice, leading to several customer complaints and potential regulatory exposure. Which of the following should be done FIRST to mitigate this risk?

Answer: A

Explanation:
When a customer-facing system is providing harmful or incorrect advice (particularly legal or financial), the " Immediate Priority " is to prevent further harm. " Introducing a human-in-the-loop review process " ensures that a qualified human checks and approves the chatbot ' s advice before it is sent to the customer. This acts as an immediate safety " guardrail. " Retraining (Options A and B) is a long-term solution that takes time and does not stop the current incorrect outputs. According to ISACA, human oversight is the most effective reactive control for mitigating high-impact errors in real-time customer interactions.


NEW QUESTION # 136
Which of the following initially provides assurance that the developer correctly interprets and identifies numerical data for balancing prior to inserting into the model?

Answer: B

Explanation:
A data dictionary (A) is the authoritative source for understanding:
Data types and numeric formats
Valid ranges and interpretations
Field definitions and business meaning
Normalization and scaling expectations
Before balancing or preprocessing data, developers must verify that they understand each feature correctly. The AAIA framework emphasizes that misinterpretation of numeric variables often leads to:
Incorrect normalization
Faulty scaling
Skewed class balancing
Inaccurate model training


NEW QUESTION # 137
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

Answer: A

Explanation:
Adversarial attacks involve " manipulated inputs " (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must " Validate the process for handling manipulated inputs. " This includes checking for robust input validation, anomaly detection on incoming data, and " adversarial training " where the model is intentionally exposed to these attacks during development to build resilience. Focusing on logs or speed (Options A, B, and C) does not address the fundamental vulnerability of the algorithm to technical manipulation.


NEW QUESTION # 138
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