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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 65% |
| Exam Format: | Multiple Choice, Remotely Proctored, Computer-Based |
| Available Languages: | English |
| Related Certifications: | CIA FCCA CISA CPA ACCA |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 90 |
| 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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NEW QUESTION # 272
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?
Answer: B
Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error-such as false customer rejection or failure to detect fraudulent accounts. The AAIA™ Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines.
KYC models must be assessed for how errors can lead to compliance failures or reputational harm." Benchmarking (B) supports best practice alignment, and incident response (C) is part of mitigation, but D addresses the most critical consequence of AI risks in banking.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Risk Impact and Business Alignment in AI Systems"
NEW QUESTION # 273
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
Answer: D
Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations-where generative models produce fabricated or misleading outputs. The AAIA™ Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI"
NEW QUESTION # 274
Which of the following is the BEST key performance indicator (KPI) to measure a model's performance on imbalanced datasets?
Answer: D
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 # 275
Which of the following is the GREATEST risk associated with using AI in audit planning?
Answer: A
Explanation:
In the context of AI-driven audit planning, the AAIATM Study Guide identifies incomplete or inaccurate data as the most significant risk. If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.
"Audit planning supported by AI tools is only as strong as the underlying data. Incomplete data may cause incorrect risk assessments or misallocate audit resources, undermining audit effectiveness." While scope creep and cost are common concerns in project management, and limited AI knowledge can be mitigated through training, only incomplete data directly impacts the validity of audit planning outputs.
NEW QUESTION # 276
Which of the following is MOST important to perform when testing an AI model for security?
Answer: C
Explanation:
AI security testing must address both traditional IT vulnerabilities and AI-specific threats.
According to the AAIATM framework, "Vulnerability Assessments" are essential for identifying weaknesses in the AI pipeline, including insecure APIs, lack of input sanitization, and susceptibility to adversarial attacks. This assessment helps auditors determine if the system can resist exploitation, such as prompt injection or data poisoning.
NEW QUESTION # 277
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