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NEW QUESTION # 22
A healthcare organization uses patient data to train an AI model for early disease detection. Which of the following practices provides the BEST assurance that personal data is secure and its integrity is maintained?
- A. Implementing strict data access controls and conducting security tests
- B. Anonymizing patient data and performing regular quality checks
- C. Updating the AI model with new data and tracking changes
- D. Encrypting stored data to reduce exposure and log access
Answer: B
Explanation:
In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).
"Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset." While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy and Security of Sensitive AI Data"
NEW QUESTION # 23
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?
- A. The model has not been retrained recently.
- B. The decision-making process is unexplainable.
- C. The solution is hosted on a shared cloud environment.
- D. The solution documentation is still in draft.
Answer: B
Explanation:
TheGREATEST concernis when the AI system'sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.
Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).
NEW QUESTION # 24
Which of the following is the PRIMARY purpose of an AI acceptable use policy?
- A. Establishing guidance on the ethical use of AI
- B. Outlining AI usage monitoring procedures
- C. Educating employees on where to find and how to use AI tools
- D. Explaining the distinction between different types of AI
Answer: A
NEW QUESTION # 25
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?
- A. Social media platform with images from all over the world
- B. Cohort of dermatologists with signed patient consent forms
- C. Open-source data augmentation files
- D. Research facility receiving grants for cancer research
Answer: A
Explanation:
Images fromsocial media platforms(C) pose the greatest risk todata integritybecause:
* Images may be heavily filtered or edited
* Lighting, scale, and resolution are inconsistent
* Metadata is unreliable
* Medical diagnosis labels (if any) are unverified
* Context is unknown and may include misinformation
* Sources cannot be validated or authenticated
AAIA stresses that medical AI systems requirehigh-quality, clinically validated datasetswith clear provenance, accuracy, and consent.
Research facilities (A) and dermatologist cohorts (D) provide medically reliable, high-integrity data.
Open-source augmentation files (B) are synthetic and secondary, but still safer than uncontrolled, unlabeled social media images.
References:
AAIA Domain 2: Data Quality, Provenance, Accuracy, and Label Integrity
AAIA Domain 1: Risk Identification in High-Impact AI Systems
NEW QUESTION # 26
When developing an audit plan, which of the following is MOST important specifically for the transparency of an AI application?
- A. Validation testing
- B. Compliance testing
- C. Regression testing
- D. Explainability testing
Answer: D
Explanation:
Explainability testing is essential for determining how and why an AI model produces specific outputs.
Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
* Stakeholders understand model decisions
* Auditors can verify fairness and accuracy
* Regulators can review decision logic
* The organization can justify outcomes to affected individuals
* Models align with ethical and governance expectations
Regression testing (B) checks consistency across versions.
Compliance testing (C) checks legal adherence.
Validation testing (D) checks accuracy.
None specifically ensure transparency.
References:
AAIA Domain 5: Explainability, Transparency, Accountability
AAIA Domain 3: Auditability of AI Systems
NEW QUESTION # 27
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?
- A. The model utilizes historical credit data to predict future credit behavior.
- B. The model incorporates the applicant's loan history to assess spending habits.
- C. The model considers the applicant's income level as a key factor in the credit decision.
- D. The model uses postal codes as a primary factor in determining creditworthiness.
Answer: D
NEW QUESTION # 28
Which of the following is the MOST important consideration when auditing the data used for training an AI model?
- A. Predictability
- B. Timeliness
- C. Representativeness
- D. Understandability
Answer: C
NEW QUESTION # 29
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?
- A. Adversarial testing
- B. Group analysis
- C. Latency testing
- D. Confusion matrix
Answer: D
NEW QUESTION # 30
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. Identifying technology-specific risk and considerations
- C. Summarizing meeting transcripts from interviews with database administrators (DBAs)
- D. Identifying separation of duties conflicts for database data changes
Answer: B
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 # 31
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?
- A. Temperature and generator effectiveness is -0.85
- B. Rotational speed and tool wear is 0.56
- C. Daily precipitation and generator efficiency is 0.09
- D. Electricity demand and machine torque is 0.0
Answer: A
Explanation:
A correlation of -0.85 (option C) represents a strong negative correlation, indicating a meaningful inverse relationship. In AI model development, high-magnitude correlations (positive or negative) strongly influence feature selection and model behavior.
AAIA emphasizes that auditors must understand correlation strength to evaluate:
* Feature relevance
* Model weight justification
* Risk of multicollinearity
* Data quality and representativeness
The other correlations are weak or negligible.
Thus, the pair with -0.85 is the most significant.
References:
AAIA Domain 1: AI Data Relationships
AAIA Domain 2: Data Understanding and Statistical Foundations
NEW QUESTION # 32
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management's PRIMARY response?
- A. Synthesize more data of the affected demographic group.
- B. Determine if audit sampling is sufficient.
- C. Accept the audit findings as within risk tolerance.
- D. Conduct comprehensive bias analysis.
Answer: D
Explanation:
A significantly higher rejection rate is a clear indicator of potentialalgorithmic discrimination. Management' s PRIMARY response should be to conduct acomprehensive bias analysis(C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help butonly afterthe root cause is identified-it is not the primary first step.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Bias, Fairness, and Transparency Evaluations.
NEW QUESTION # 33
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?
- A. The measures in place to ensure the appropriateness and relevance of input data for generative AI models
- B. The frequency of data audits verifying the integrity and accuracy of inputs
- C. The algorithms for generative AI models designed to detect and correct data bias before processing
- D. The effectiveness of data anonymization processes that help preserve data quality
Answer: A
NEW QUESTION # 34
An AI healthcare diagnostic tool requires large volumes of patient data, raising concerns about privacy and data breaches. Which of the following is the MOST effective strategy to mitigate this risk?
- A. Encrypt the data and transmit it through a secure channel.
- B. Use synthetic data or anonymized data sets for model training.
- C. Collect data from all patients to use for data analysis.
- D. Limit the tool's access to only publicly available datasets.
Answer: B
Explanation:
The most effective strategy to protect sensitive patient data is to use synthetic data or anonymized datasets for model training. This reduces exposure of personally identifiable information while allowing the model to learn meaningful medical patterns.
AAIA emphasizes privacy-by-design, de-identification, and minimal use of raw personal data in high-risk sectors such as healthcare. Anonymization and synthetic data significantly reduce the risk of re-identification or breach-related harm.
Option A (encryption) protects data in transit but does not eliminate privacy risks. Option B is impractical because healthcare models require clinically relevant datasets, not public data. Option C increases data exposure, aggravating privacy risks.
Thus, using anonymized or synthetic data is the strongest privacy protection aligned with healthcare compliance principles.
References:
AAIA Domain 5: Data Privacy, AI Ethics, and Compliance.
AAIA Domain 2: Data Management Practices for Sensitive AI Use Cases.
NEW QUESTION # 35
An IS auditor for a veterinary clinic was informed that the dog breed categorical variable is necessary for the predictive model. Which of the following introduces the MOST risk?
- A. Data scaling was not utilized.
- B. Ordinal label encoding was utilized.
- C. Clustering was not utilized.
- D. One-hot encoding was utilized.
Answer: B
Explanation:
Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3). This falsely implies hierarchy or ranking among breeds, introducing bias into the model.
AAIA emphasizes that incorrect encoding of categorical variables can cause:
* Artificially weighted importance
* Misleading correlations
* Incorrect predictions based on false orderFor non-ordered categories,one-hot encoding(D) is the correct, low-risk approach.Lack of scaling (A) is not directly relevant for categorical fields. Clustering (B) is unrelated.Thus, ordinal encoding introduces the highest risk.
References:
AAIA Domain 2: Data Encoding, Feature Engineering, and Bias Avoidance.
NEW QUESTION # 36
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?
- A. Disparate impact analysis
- B. Regression testing
- C. Cross-cluster analysis
- D. Predictive analytics
Answer: A
Explanation:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.
NEW QUESTION # 37
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
- A. It reduces the need for human oversight, ensuring seamless and autonomous data governance.
- B. It focuses on enhancing the accuracy and reliability of AI model predictions.
- C. It accelerates AI implementation timelines by fully automating data preparation processes.
- D. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations.
Answer: D
Explanation:
According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle." While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Governance Frameworks and Compliance"
NEW QUESTION # 38
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. Failure to identify anomalies that can bias training data
- C. Inadequate staff training on the use of the system
- D. Infrequent updates to anomaly detection algorithms
Answer: B
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 # 39
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?
- A. AI model configuration testing
- B. AI data set anonymization
- C. Monitoring of AI access logs
- D. Monitoring of AI model developers
Answer: A
Explanation:
Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIA™ Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness.
"Model configuration testing simulates real-world threat conditions to validate model resilience. This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs." While access monitoring (C) and anonymization (A) reduce risks, they don't actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Security and Resilience Testing for AI Models"
NEW QUESTION # 40
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?
- A. One-hot encoding the data attribute customer rewards category for the options economy, business, first class
- B. Creating dummy variables for the data attribute dog breed for the options labrador, terrier, beagle
- 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 product flavor for the options vanilla, chocolate, strawberry, banana
Answer: A
NEW QUESTION # 41
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