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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Passing Score:65%
Related Certifications:CIA
CPA
CISA
ACCA
FCCA
Available Languages:English
Exam Duration:120 minutes
Real Exam Qty:90
Exam Format:Remotely Proctored, Computer-Based, Multiple Choice
Sample Questions:ISACA AAIA Sample Questions
Exam Way:Online remotely proctored computer-based exam
Pre Condition:Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:https://www.isaca.org/credentialing/aaia

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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.

ISACA Advanced in AI Audit Sample Questions (Q280-Q285):

NEW QUESTION # 280
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management ' s PRIMARY response?

Answer: A

Explanation:
A significantly higher rejection rate is a clear indicator of potential algorithmic discrimination .
Management's PRIMARY response should be to conduct a comprehensive bias analysis (C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help but only after the root cause is identified-it is not the primary first step.
References:
ISACA, AAIA Exam Content Outline - Domain 1: Bias, Fairness, and Transparency Evaluations.


NEW QUESTION # 281
An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?

Answer: B

Explanation:
Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIATM Study Guide, when input exceeds token limits, the model processes only the initial portion--leading to biased outputs based on early entries.
"Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset."


NEW QUESTION # 282
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: C

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 # 283
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: A

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.


NEW QUESTION # 284
The PRIMARY objective of machine learning (ML) in data processing is to:

Answer: C


NEW QUESTION # 285
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