AAIA Test Study Guide, AAIA Practice Questions

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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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AAIA Practice Questions, AAIA Valid Exam Registration

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ISACA Advanced in AI Audit Sample Questions (Q285-Q290):

NEW QUESTION # 285
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?

Answer: B

Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIA™ manual identifies " Adversarial Training " as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs. While static filtering (Option A) and ensembles (Option C) can provide layers of defense, they are often bypassed by sophisticated attacks. Regular bias reviews further ensure that the model's decision-making remains fair and consistent across all inputs, including those designed to exploit algorithmic weaknesses.


NEW QUESTION # 286
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

Answer: A


NEW QUESTION # 287
Which regulatory framework introduces a risk-tiered classification (unacceptable, high, limited, minimal) for AI systems?

Answer: D

Explanation:
The EU AI Act classifies AI systems into risk tiers, with high-risk systems subject to conformity assessments, documentation, and human oversight requirements.


NEW QUESTION # 288
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?

Answer: A

Explanation:
Bias in AI models is most commonly introduced through the training data. The AAIATM Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results."


NEW QUESTION # 289
An organization's system development process has been enhanced with AI. Which of the following features presents the GREATEST risk?

Answer: C

Explanation:
Allowing AI to autonomously generate code without human review introduces significant risks, including security vulnerabilities, logic errors, and noncompliance with organizational development standards. The AAIA™ Study Guide strongly advocates for human-in-the-loop oversight, particularly in automated development contexts.
"AI-assisted development must include manual code reviews to ensure functionality, compliance, and security. Autonomous code generation without validation increases the risk of introducing undetected flaws." While A, B, and C involve operational risks or inefficiencies, only D constitutes a direct breach of secure development life cycle principles.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "AI in Software Development and Associated Risks"


NEW QUESTION # 290
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