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It is similar to the AAIA desktop-based software, with all the elements of the desktop practice exam. This AAIA exam can be accessed from any browser and does not require installation. The AAIA questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based AAIA Practice Test immediately through any operating system and browsers.
| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Related Certifications: | CPA ACCA CISA CIA FCCA |
| Passing Score: | 65% |
| Real Exam Qty: | 90 |
| Exam Format: | Multiple Choice, Computer-Based, Remotely Proctored |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| 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 |
>> ISACA AAIA Exam Question <<
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NEW QUESTION # 287
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: D
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 # 288
Which of the following is the MOST important risk for an IS auditor to consider when reviewing the adoption of an AI system?
Answer: A
Explanation:
Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIATM Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.
"Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place."
NEW QUESTION # 289
Which of the following techniques would be MOST effective as part of incident management procedures for a prompt injection attack?
Answer: C
Explanation:
Prompt injection attacks involve maliciously crafted inputs intended to override system instructions, exfiltrate data, or cause harmful behavior. The most effective control aligned with incident management is to deploy robust input validation and sanitization (C), which includes rules and filters designed to detect and neutralize potentially malicious content before it reaches the model. AAIA's coverage of AI threats and vulnerabilities highlights the importance of input validation and secure prompt handling for generative AI systems.
NEW QUESTION # 290
An IS auditor reviewed an AI-enabled software for processing a bank's financial information and discovered errors in the training data. Which of the following would BEST mitigate this risk?
Answer: B
Explanation:
When errors are discovered in training data, the primary risk is that the model has learned from incorrect or low-quality inputs, which can systematically distort outputs (e.g., misstatements of financial information, incorrect classifications, or erroneous flags). Data quality testing directly targets this issue by assessing completeness, accuracy, consistency, validity, and integrity of the data used to train, validate, and test AI models.
NEW QUESTION # 291
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: D
Explanation:
Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.
NEW QUESTION # 292
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