AAIA Exam Question, Valid AAIA Test Book

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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Certificate Validity Period:	3 years
Exam Price:	US$459 (member) / US$599 (non-member)
Related Certifications:	ACCA FCCA CIA CPA CISA
Available Languages:	Spanish, Chinese, English
Real Exam Qty:	90
Passing Score:	450 (scaled, range 200–800)
Exam Duration:	150 minutes
Exam Format:	Scenario-based questions, Multiple-choice questions, Computer-based testing
Recommended Training:	AAIA Official Review Course AAIA Review Manual
Exam Registration:	ISACA Official Registration
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:	Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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Valid AAIA Test Book - AAIA Study Guides

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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 (Q270-Q275):

NEW QUESTION # 270
Which of the following pre-processing steps would MOST effectively justify an AI model ' s decision to a non-technical stakeholder?

A. Local interpretable model-agnostic explanations (LIME)
B. Partial dependence plots
C. Permutation feature importance
D. AI model penetration testing

Answer: A

Explanation:
While all the listed techniques (except penetration testing) support interpretability, " LIME " is specifically noted in the ISACA AAIA™ Study Guide for its ability to explain individual decisions. LIME creates a simpler, interpretable model around a specific prediction to show which features (e.g., high income or low debt) were the primary drivers for that specific case. This " Local " explanation is much easier for non- technical stakeholders or customers to understand than " Global " metrics like feature importance (Option A) or partial dependence plots (Option C), which describe the model ' s behavior as a whole.

NEW QUESTION # 271
Which of the following is the GREATEST benefit of using large language models (LLMs) to identify potential control deficiencies?

A. Allowing the auditor to replace walkthroughs with automated testing
B. Independently verifying audit options based on control assessments
C. Summarizing multiple process narratives for inconsistencies
D. Simulating financial transactions for internal control testing

Answer: C

Explanation:
LLMs excel at processing large amounts ofunstructured text(e.g., policies, process narratives, procedures).
TheGREATEST benefitis their ability tosummarize and compare multiple documents to identify inconsistencies(B), which can signal potential control weaknesses or misalignments. This aligns with AAIA's description of AI-enabled audit analytics that supportdocument analysis, narrative comparison, and pattern detectionfor control assessment.
Option A presumes autonomy, which is unsafe without validation. Option C is more aligned with predictive or simulation models, not LLM strengths. Option D is not acceptable because walkthroughs require professional judgment and evidence gathering; AI should support, not replace. Thus,summarization and inconsistency identificationis the most practical and impactful benefit.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes (use of LLMs for narrative analysis).

NEW QUESTION # 272
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card - Electrical Grid Predictive Maintenance Model
Model Information:
Description: AI model designed to predict maintenance needs for
electrical grid components, reduce unplanned downtime, and improve grid reliability.
Inputs: Real-time sensor data, historical maintenance records, and
operational logs.
Outputs: Maintenance needs predictions for 60 & 90 days.
Evaluation:
Approach: Cross-validation and validation of accuracy, precision, and
recall.
Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%

A. The AI model correctly predicts maintenance needs 95% of the time.
B. The electrical grid uptime is expected to be 72% of the time.
C. Grid failure is predicted to occur after 90 days.
D. F1 indicates that the model identifies true maintenance needs 76% of the time.

Answer: D

Explanation:
The F1 score is the harmonic mean of precision and recall, offering a balanced measure of model accuracy, especially when there is an imbalance in the classes (e.g., more "no-maintenance" than "needs-maintenance" outcomes). According to the AAIATM Study Guide, the F1 score is used to evaluate how well the model identifies true positives while balancing the risk of false positives and false negatives.
"An F1 score summarizes the model's ability to correctly identify relevant events--in this case, true maintenance needs. A 76% F1 score means the model is relatively balanced and effective at catching maintenance requirements without generating too many false alerts."

NEW QUESTION # 273
An organization deployed an AI-powered customer service chatbot trained using customer chat logs. During a risk assessment, which issue should be the IS auditor's GREATEST concern?

A. Limited AI model capability to incorporate new data
B. Obsolete procedures leading to inadequate data integrity validation
C. Reputational impacts from inaccurate chatbot responses
D. Insufficient access controls leading to unauthorized customer data exposure

Answer: D

Explanation:
The GREATEST concern is insufficient access controls (D), which can lead to unauthorized exposure of customer data--a severe privacy, security, regulatory, and reputational risk. Chat logs often contain personally identifiable information and sensitive communications. AAIA prioritizes data confidentiality, access control, and privacy obligations as highest-risk elements, particularly for customer-interactive AI systems.

NEW QUESTION # 274
An IS auditor uses an internally developed generative AI tool to prepare a status update for audit stakeholders. Which of the following is the auditor's MOST appropriate course of action?

A. Assess whether the information provided is complete and accurate.
B. Regenerate the results to ensure similar outputs are provided.
C. Share and review the results with management.
D. Compare results with a publicly available generative AI tool to ensure outputs are similar.

Answer: A

Explanation:
Auditors using AI tools to generate reports or updates must ensure the output is reliable, factually accurate, and complete. As per the AAIATM Study Guide, accountability for audit content remains with the auditor, even when generative AI assists with content creation. Therefore, verifying the completeness and correctness of AI-generated content is the primary responsibility.
"AI-generated audit outputs must be validated against source data and professional standards.
The auditor must critically assess AI contributions and not rely on them without human oversight."

NEW QUESTION # 275
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