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ISACA Advanced in AI Audit Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which of the following should be done FIRST when developing an incident management process for AI threats?

A. Establish incident classification procedures
B. Configure SIEM for security alerts
C. Develop incident escalation procedures
D. Define clear roles and responsibilities

Answer: D

Explanation:
The AAIA framework states that incident response begins withroles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
* Ownership of monitoring
* Chain of command for incident decisions
* Clear responsibility for documentation
* Communication pathways
* Allocation of resources for containment
Classification (A), escalation (D), and SIEM configuration (C) follow AFTER roles are assigned. Therefore, defining roles and responsibilities is foundational.
References:
AAIA Domain 2: AI Incident Management
AAIA Domain 1: Governance and Accountability Structures

NEW QUESTION # 91
An IS auditor is evaluating an organization's incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?

A. The program uses past AI-related incidents and resolutions to categorize current incidents.
B. The program mandates retraining AI systems after incidents are investigated.
C. The program includes processes to respond to AI model drift and data integrity attacks.
D. The program prioritizes incidents based on alignment with industry leading practices.

Answer: C

NEW QUESTION # 92
Which of the following is the GREATEST risk associated with using AI in audit planning?

A. Incomplete data
B. Limited knowledge
C. Scope creep
D. Increased planning costs

Answer: A

Explanation:
In the context of AI-driven audit planning, the AAIA™ 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 If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.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.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Data Integrity and Planning Risks"

NEW QUESTION # 93
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

A. Sensitivity and origin of training data
B. Frequency of model updates
C. Cleaning and validation methods for training data
D. Ability to generate diverse outputs

Answer: A

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 # 94
Which of the following BEST detects model drift or unexpected changes in AI model outputs?

A. AI model retraining
B. Anomaly monitoring
C. AI model documentation reviews
D. Standardization of AI configurations

Answer: B

Explanation:
Anomaly monitoring detects irregularities or deviations in input data or model outputs, which are key indicators of model drift. According to the AAIA™ Study Guide, continuous anomaly detection is one of the most effective methods for identifying when a model is no longer functioning as expected due to changes in the data environment.
"Monitoring for output anomalies enables early identification of model drift. This proactive approach allows organizations to retrain or adjust models before significant performance degradation occurs." Configuration standardization (A) and documentation reviews (C) support governance but don't detect changes. Retraining (D) is a remediation step, not a detection mechanism. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Drift Detection and Anomaly Monitoring"

NEW QUESTION # 95
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AAIA Vce File | 100% Free Updated Valid ISACA Advanced in AI Audit Test Pass4sure

Free PDF 2025 ISACA AAIA: Perfect ISACA Advanced in AI Audit Vce File

ISACA Advanced in AI Audit Sample Questions (Q90-Q95):

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