UPDATED [2026] Pass ISACA AAIA Exam in First Attempt Guaranteed
Pass AAIA Exam Latest Practice Questions
NEW QUESTION # 78
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?
- A. Presenting theoretical situations to justify the reason for asking the questions
- B. Submitting the same questions in a foreign language translated by another AI-based system
- C. Randomly placing keywords unrelated to the main topic
- D. Asking the same questions later when the algorithm has changed after further learning
Answer: A
NEW QUESTION # 79
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?
- A. Developing architecture diagrams
- B. Summarizing meeting transcripts from interviews with database administrators (DBAs)
- C. Identifying separation of duties conflicts for database data changes
- D. Identifying technology-specific risk and considerations
Answer: D
Explanation:
Generative AI excels at synthesizing large datasets and technical documentation into understandable insights.
The AAIA™ Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.
"AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping." While summarizing interviews (D) and creating diagrams (B) are helpful, only C directly informs audit planning with actionable intelligence. A (separation of duties) is a later-stage control assessment.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Use in Planning and Scoping"
NEW QUESTION # 80
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?
- A. To ensure the systems align with the preservation of individual rights
- B. To identify and mitigate potential data drift within models
- C. To improve the accuracy and performance of the systems
- D. To align AI system development with organizational values and principles
Answer: A
Explanation:
The AAIA™ Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.
"Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process.
This is foundational in building public trust and avoiding legal liabilities." Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Ongoing Ethical Oversight"
NEW QUESTION # 81
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?
- A. Computational logic is based on probabilities.
- B. Neural node access varies each time the process is executed.
- C. Limitations can arise in the quantification of risk profiles.
- D. Servers are reconfigured periodically.
Answer: A
NEW QUESTION # 82
An insurance company uses an AI model to set premium rates. To align with AI-related policies on fairness, which of the following is the FIRST course of action?
- A. Allowing customers to contest premium rates provided by the AI model
- B. Modifying the AI model's training dataset to address potential biases
- C. Training alternate AI models and comparing biases with the primary model
- D. Reviewing AI model training data to identify potential biases
Answer: D
Explanation:
AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, thefirstcourse of action is to review training data (option B) to identify:
* Skewed demographic distributions
* Missing or underrepresented populations
* Inappropriate use of sensitive attributes
* Incorrect labels or improperly encoded variables
* Historical decisions that may propagate discrimination
Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).
Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.
AAIA emphasizes addressing bias at theroot-the training data-before adjusting the model or outputs.
References:
AAIA Domain 5: Fairness, Bias Identification, Ethical AI Principles
AAIA Domain 2: Data Governance and Input Evaluations
NEW QUESTION # 83
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?
- A. Lack of regular quality reviews for training data
- B. Inadequate staff training on the use of the system
- C. Infrequent updates to anomaly detection algorithms
- D. Failure to identify anomalies that can bias training data
Answer: D
Explanation:
In ahigh-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. TheGREATEST concernfor an IS auditor is when the processfails to identify anomalies that can bias training data(A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.
Option B (lack of regular quality reviews) is serious but is partially addressed if anomaly detection is effective. Option C (infrequent updates) may degrade detection performance over time but is less critical than outright failure to detect harmful anomalies. Option D (staff training) is important for operational effectiveness but still secondary to the technical failure to catch bias-inducing anomalies. AAIA stresses that data integrity and monitoring controlsare paramount in high-risk contexts.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions, data monitoring, and anomaly detection).
ISACA AI risk materials focusing on high-risk AI oversight and data integrity.
NEW QUESTION # 84
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
- A. Data validation processes are automated and consistently performed.
- B. Synthetic data is used to train the AI systems.
- C. Training data is manually reviewed for bias.
- D. Training data remains relevant and reflects evolving real-world conditions.
Answer: D
Explanation:
Representativeness means that training data accurately reflects thecurrent real-world environmentin which the AI system operates. The BEST way to ensure this is by verifying that thetraining data remains relevant and aligned with evolving real-world conditions(C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
Manual review (A) is limited in scope and scale. Automated validation (B) helps detect errors but does not ensure data reflects the real world. Synthetic data (D) supplements but does not guarantee representativeness unless calibrated properly. Therefore,continuous relevance and contextual alignmentis the most important factor.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data relevance, drift detection, representativeness).
NEW QUESTION # 85
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?
- A. Specificity
- B. Precision
- C. Accuracy
- D. Recall
Answer: D
Explanation:
In predictive maintenance use cases-such as detecting turbine failure-the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA™ Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Evaluation Metrics and Predictive Accuracy"
NEW QUESTION # 86
Which of the following is the PRIMARY purpose of an AI acceptable use policy?
- A. Outlining AI usage monitoring procedures
- B. Educating employees on where to find and how to use AI tools
- C. Establishing guidance on the ethical use of AI
- D. Explaining the distinction between different types of AI
Answer: C
Explanation:
An AI acceptable use policy (AUP) defines how AI tools and technologies should be ethically and responsibly used within an organization. According to the AAIA™ Study Guide, the primary goal of an AUP is to prevent misuse and promote adherence to ethical, legal, and operational standards.
"An AI acceptable use policy provides governance over how AI tools may be used, especially regarding data handling, fairness, and prohibited uses. It aligns employee actions with organizational values and compliance requirements." Monitoring procedures (B), training (C), and taxonomy explanations (D) may be included in broader AI documentation, but the AUP's core purpose is ethical usage governance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Policies, Standards, and Ethical Frameworks for AI"
NEW QUESTION # 87
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
- A. To confirm that the AI system can handle high volumes of customer queries
- B. To ensure the model's pricing logic aligns with business strategy
- C. To ensure the system integrates seamlessly with legacy booking platforms
- D. To verify that pricing decisions do not result in discriminatory outcomes
Answer: D
Explanation:
Dynamic pricing algorithms can unintentionally discriminate against protected groups if trained on biased data or poorly designed features. The AAIA highlights fairness testing as a mandatory requirement in any AI solution that impacts customers financially or socially.
Specific ethical tests are needed to ensure:
* Pricing does not vary unfairly based on demographics
* Sensitive attributes ( ethnicity, age, gender ) are not inferred or misused
* Customer segmentation does not disproportionately disadvantage protected groups
* Historical biases do not propagate into automated pricing
Options A, B, and C are important development tasks but do not address the highest-risk area: preventing discriminatory pricing. Fairness evaluation is a critical AAIA requirement.
References:
AAIA Domain 5: Ethical AI, Fairness, Discrimination Testing
AAIA Domain 1: AI Governance and Impact Assessments
NEW QUESTION # 88
A healthcare AI tool recommends treatments with high success rates but significant risk. The hospital prioritizes patient safety over innovation. What is the BEST course of action?
- A. Obtain patients' consent for the use of their data by the AI tool.
- B. Adjust the AI's parameters to align with the hospital's risk tolerance.
- C. Use the AI tool only for low-risk situations.
- D. Discontinue using the AI tool and rely solely on doctor expertise.
Answer: B
Explanation:
AI systems must align with theorganization's risk appetite and ethical principles, especially in healthcare where patient safety is paramount. The BEST action is toadjust the AI's parameters(A) so the recommendations reflect the hospital's conservative risk tolerance, reducing the frequency of high-risk suggestions. AAIA stresses AI governance alignment with organizational risk appetite, treatment guidelines, and ethical priorities.
Option B is overly disruptive and eliminates beneficial AI capabilities. Option C is necessary for privacy but does not address treatment safety. Option D limits utility but doesn't correct underlying alignment issues. The core issue ismodel alignment with ethical and safety standards, making option A the correct choice.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical Principles in AI (alignment with risk tolerance, safety, beneficence).
NEW QUESTION # 89
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. The hyperparameters are not optimized.
- B. Data versioning controls were not developed.
- C. Compute scale training was not performed.
- D. Consent is not properly managed.
Answer: A
NEW QUESTION # 90
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?
- A. Whether data leakage occurred from utilizing overlapping records in the data sets
- B. Whether a sufficient number of records were utilized in the training data set
- C. Whether the validation data set utilized the same number of records as the training data sets
- D. Whether the training, validation, and testing data sets were created in the correct order
Answer: A
NEW QUESTION # 91
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?
- A. Validating AI incident response plans
- B. Determining key risk indicators (KRIs)
- C. Fostering security awareness
- D. Identifying control gaps
Answer: D
Explanation:
Adversarial testinginvolves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. Theprimary objectiveis to discoverweaknesses and control gaps(D) in the model and its surrounding processes-such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.
While results from adversarial testing may inform incident response planning (A), KRI definition (B), or security awareness (C), those are secondary benefits. AAIA's coverage of AI threats and vulnerabilities emphasizes adversarial testing as acontrol validation and gap-identification mechanism, directly addressing AI-specific risk exposure.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Threats and Vulnerabilities Specific to AI; Testing Techniques for AI Solutions.
ISACA guidance on adversarial testing and AI security posture assessment.
NEW QUESTION # 92
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?
- A. Data contamination due to biased AI model outputs
- B. Potential business disruptions
- C. Excessive reliance on AI-generated insights
- D. Unauthorized data disclosure
Answer: D
NEW QUESTION # 93
The PRIMARY objective of machine learning (ML) in data processing is to:
- A. Perform actions that would typically require human intelligence.
- B. Analyze data sets to identify visual patterns and trends.
- C. Draw statistical inferences for creating artificial human intelligence.
- D. Enhance the explainability of AI model outputs.
Answer: A
NEW QUESTION # 94
Which of the following considerations should be prioritized when using an AI tool to select a sample for conducting an audit of a financial institution's transaction processing system?
- A. The speed of sample generation
- B. The historical performance in previous audits
- C. The transparency of the sampling process
- D. The ability to process large volumes of data
Answer: C
Explanation:
In an audit context,transparencyof sampling is essential for demonstrating that the sample is fair, unbiased, and aligned with the audit objectives. When an AI tool selects samples for testing financial transactions, auditors must be able to explain and defendhowthe sample was generated-particularly to management, regulators, and external stakeholders. Option A directly supports AAIA's focus onaudit planning, sampling methodologies, and AI audit evidence.
High throughput (option B) and speed (option C) are beneficial but secondary to methodological soundness and explainability. Option D (historical performance) can be helpful but does not guarantee current transparency or appropriateness in new contexts. For AI-enabled sampling, the priority is that theselection logic is understandable, documented, and reproducible, ensuring audit defensibility.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (Audit Testing and Sampling Methodologies; Audit Evidence Collection Techniques).
ISACA auditing guidance on sampling and transparency in AI-assisted audit procedures.
NEW QUESTION # 95
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?
- A. Developing action items discussed in closing meetings for management action plans
- B. Revising audit background and scope information based on new information from management
- C. Developing a draft of an executive summary based on detailed findings and audit scope
- D. Revising audit conclusions with precise verbiage to describe the audit observations
Answer: C
NEW QUESTION # 96
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?
- A. Suspend the use of the platform until the training data is sanitized.
- B. Label all AI-generated images to disclaim the possibility of third-party content.
- C. Implement a manual review process to ensure no copyrighted images are used in generated outputs.
- D. Use a platform that certifies the provenance and licensing of its training data.
Answer: D
Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"
NEW QUESTION # 97
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?
- A. Creating dummy variables for the data attribute product flavor for the options vanilla, chocolate, strawberry, banana
- B. One-hot encoding the data attribute customer rewards category for the options economy, business, first class
- C. One-hot encoding the data attribute car colors for the options red, blue, green, black, white
- D. Creating dummy variables for the data attribute dog breed for the options labrador, terrier, beagle
Answer: B
NEW QUESTION # 98
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