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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
  • 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 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 (Q246-Q251):

NEW QUESTION # 246
Which of the following is the BEST key performance indicator (KPI) to measure a model's performance on imbalanced datasets?

Answer: B

Explanation:
For imbalanced datasets, standard "accuracy" or "error rate" is misleading (e.g., if 99% of transactions are legitimate, a model that predicts "legitimate" every time is 99% accurate but useless for fraud detection). The "F1 score" is the harmonic mean of Precision and Recall, providing a balanced metric that considers both false positives and false negatives. It is the industry-standard KPI for evaluating how well a model handles the minority class. While Precision and Recall are valuable individually, the F1 score provides a single, comprehensive measure of the model's effectiveness in skewed environments.


NEW QUESTION # 247
Which of the following should be done FIRST when an AI chatbot has been identified as giving harmful advice?

Answer: A

Explanation:
When a system is actively producing harmful content, the priority is "immediate containment."
"Enabling content output filters" is a critical safety control that acts as a "guardrail," blocking the harmful responses before they reach the end-user. While prompt sanitization (Option A) targets the input, harmful advice often stems from the model's internal logic regardless of the input.
Output filters provide the fastest mitigation for protecting users and the organization's reputation.
Once the immediate risk is contained, the organization can conduct a root-cause analysis and perform more permanent retraining or access segmentation.


NEW QUESTION # 248
An IS auditor relies on generative AI to document audit narratives from walkthrough meetings. The final version includes generic language and misses key exceptions. What safeguard could improve the reliability of AI-generated documents?

Answer: C

Explanation:
When generative AI produces generic or " vague " summaries, it is often because it lacks the specific context of the meeting. " Prompt engineering and contextual anchoring " involve providing the AI with clear instructions (e.g., " focus on control exceptions " ) and anchoring the request in specific data (e.g., " refer only to the attached meeting transcript " ). According to ISACA, this technique directs the model ' s attention and reduces the likelihood of hallucinations or omissions. Increasing " temperature " (Option A) would actually make the model more random and less reliable. Anchoring ensures the AI stays grounded in the actual facts of the audit walkthrough.


NEW QUESTION # 249
An IS auditor reviewed an AI-enabled software for processing a bank's financial information and discovered errors in the training data. Which of the following would BEST mitigate this risk?

Answer: D

Explanation:
When errors are discovered in training data, the primary risk is that themodel has learned from incorrect or low-quality inputs, which can systematically distort outputs (e.g., misstatements of financial information, incorrect classifications, or erroneous flags).Data quality testingdirectly targets this issue by assessing completeness, accuracy, consistency, validity, and integrity of the data used to train, validate, and test AI models.
Functional testing (A) focuses on whether the application behaves according to requirements, not specifically on the underlying data quality. User interface testing (C) concerns usability and presentation, not training data. Model validation on benchmark data (D) helps assess performance but may not reveal or correct root# cause issues in the underlying training data if those benchmarks are also flawed or unrepresentative. AAIA content emphasizes thatdata quality controls are foundationalto AI reliability and must be treated as a core operational control.
References:
ISACA,Advanced in AI Audit (AAIA) Exam Content Outline- Domain 2: Data Management Specific to AI (data quality, data balancing, data security).
ISACA AI guidance on data quality controls as the basis of trustworthy AI outcomes.


NEW QUESTION # 250
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: C

Explanation:
In a high-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. The GREATEST concern for an IS auditor is when the process fails 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.


NEW QUESTION # 251
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