ISACA AAIA Valid Test Materials - AAIA Latest Study Questions

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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Related Certifications:	CIA ACCA FCCA CISA CPA
Exam Format:	Scenario-based questions, Multiple-choice questions, Computer-based testing
Exam Duration:	150 minutes
Exam Price:	US$459 (member) / US$599 (non-member)
Passing Score:	450 (scaled, range 200–800)
Available Languages:	English, Chinese, Spanish
Real Exam Qty:	90
Certificate Validity Period:	3 years
Recommended Training:	AAIA Review Manual AAIA Official Review Course
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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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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.

ISACA Advanced in AI Audit Sample Questions (Q219-Q224):

NEW QUESTION # 219
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

A. Data versioning controls were not developed.
B. Consent is not properly managed.
C. The hyperparameters are not optimized.
D. Compute scale training was not performed.

Answer: C

NEW QUESTION # 220
After AI training data has been tested for biases, which of the following is MOST important to check to validate the effectiveness of the testing?

A. Sensitive information from users is securely masked before input
B. AI processes will meet expected service turnaround time
C. Possible impacts from AI outputs remain within the acceptable risk level
D. Feedback on data validation is obtained from key stakeholders

Answer: C

Explanation:
Even after identifying and mitigating bias, organizations must ensure that AI outputs do not create unacceptable risks.
AAIA emphasizes that bias mitigation must result in:
Fair outcomes
Justifiable predictions
No disproportionate harm to any demographic group
Alignment with organizational risk tolerance
Option B reflects this requirement, ensuring that the model's real-world impact aligns with documented risk thresholds.

NEW QUESTION # 221
When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?

A. Ensure a minimum of three quotes have been obtained for market research and comparison.
B. Use the vendor only if the contract has been reviewed by the information security department.
C. Only use models from vendors with globally recognized accreditation.
D. Establish responsibility and clear terms for model updates and support.

Answer: D

Explanation:
When organizations leverage off-the-shelf AI models, effective vendor management is critical to ensure operational reliability, compliance, and long-term support. The ISACA Advanced in AI AuditTM (AAIATM) Study Guide highlights that the "establishment of clear contractual terms regarding responsibilities for ongoing model updates, maintenance, support, and incident response is essential for managing third-party AI risks." By clearly defining the roles and expectations for updates and support (option B), organizations reduce the risk of unaddressed vulnerabilities, outdated models, or unclear recourse in the event of an incident or system failure. This approach supports ongoing risk management and ensures that both parties understand their obligations throughout the model's lifecycle.
While market research, vendor accreditation, and contract review by information security are important due diligence steps, they do not directly address the need for clarity in ongoing vendor responsibilities, which is critical for effective governance and sustained operation of AI solutions.

NEW QUESTION # 222
Which of the following techniques is MOST appropriate for model hyperparameter tuning?

A. Bayesian optimization
B. F1 score analysis
C. Log-rank test
D. Analysis of variance

Answer: A

Explanation:
Hyperparameter tuning is the process of finding the optimal settings (like learning rate or depth) for an AI model. "Bayesian optimization" is an efficient, automated technique that builds a probability model of the objective function to select the most promising hyperparameters to test next. Unlike simple "grid search," it saves significant time and computational cost. F1 score analysis is a way to measure the result of the tuning, but it is not the tuning method itself.
Bayesian optimization is favored in advanced AI development for its ability to find optimal configurations in complex, multi-dimensional spaces.

NEW QUESTION # 223
During an audit of a bank ' s AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?

A. Prepare an audit report.
B. Evaluate appeal processes.
C. Conduct an explainability assessment.
D. Evaluate transparency controls.

Answer: D

Explanation:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision-making. If an auditor finds that applicants were not informed, the immediate " First " step is to " Evaluate transparency controls " to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).

NEW QUESTION # 224
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