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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Related Certifications: | FCCA CISA CIA CPA ACCA |
| Real Exam Qty: | 90 |
| Exam Format: | Remotely Proctored, Computer-Based, Multiple Choice |
| Passing Score: | 65% |
| 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 # 177
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?
Answer: A
NEW QUESTION # 178
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?
Answer: D
Explanation:
Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.
AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI.
Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.
Option A (diverse outputs) relates to performance, not ethics. Option C (update frequency) is part of lifecycle management, not ethical sourcing. Option D (cleaning methods) ensures quality but does not address ethical risk if the underlying data is inappropriate or unlawfully sourced.
Thus, sensitivity and origin of training data are the primary ethical factors.
References:
AAIA Domain 5: Ethical Principles, Fairness, Data Sensitivity Considerations.
AAIA Domain 1: Privacy and Data Governance Programs.
NEW QUESTION # 179
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?
Answer: B
Explanation:
When a dataset lacks sufficient examples of a "minority class" (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to "Augment the data through synthesizing" (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain.
NEW QUESTION # 180
Which of the following are the MOST appropriate stages in the AI life cycle for evaluating edge cases?
Answer: C
Explanation:
Evaluating edge cases--rare but critical scenarios where AI may behave unpredictably--must be done during the test and verify stage (D). This phase is designed to simulate extreme or unusual inputs, validate performance under stress, and ensure robustness and safety before deployment.
AAIA highlights that robustness testing, including edge case evaluation, is a key testing technique for AI solutions.
NEW QUESTION # 181
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?
Answer: C
Explanation:
A numeric field stored as an object (string) format (option C) indicates improper data typing. Models cannot correctly compute correlations or statistical relationships when numerical values are stored as textual data.
AAIA emphasizes that incorrect datatypes are one of the most common causes of ML model misbehavior, including:
* Failure to compute averages, correlations, or mathematical operations
* Silent errors in preprocessing
* Skewed learning patterns
* Incorrect feature importance evaluationsThus, the Final Grade Percent field in object format is the most significant indicator of processing risk.Options A, B, and D are valid datatypes for their respective fields and pose no inherent model processing risk.
References:
AAIA Domain 2: Data Quality, Data Types, and Preprocessing.
AAIA Domain 3: AI Readiness and Data Validation.
NEW QUESTION # 182
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