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ISACA Advanced in AI Audit Sample Questions (Q82-Q87):

NEW QUESTION # 82
During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization's risk tolerance. Which of the following is the MOST appropriate response?

A. Obtain board approval for an exception.
B. Revisit the risk tolerance to ensure it is appropriate.
C. Enhance the data that the model is trained on.
D. Postpone deployment until the risk can be safely managed.

Answer: D

NEW QUESTION # 83
Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?

A. Number of users interacting with the AI model
B. AI model accuracy in predicting actual outcomes
C. Frequency of AI model retraining
D. Cost of resources required for AI model training

Answer: B

NEW QUESTION # 84
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

A. Training models on historical audit results generated prior to AI adoption
B. Utilizing unstructured data sources without standardized preprocessing
C. Embedding AI audit tools in transactional systems without user training
D. Applying automated anomaly detection without human oversight

Answer: B

Explanation:
Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.
AAIA warns that improperly processed unstructured data leads to:
* Incorrect text extraction
* Lost contextual meaning
* Feature extraction errors
* Misclassification
* Inaccurate audit evidence
Option B is a bias or relevance risk, not data quality.
Option C is a governance/training issue.
Option D is an oversight risk, not a data quality issue.
Therefore, using unstructured data without preprocessing is the most direct threat to data quality.
References:
AAIA Domain 2: Data Preprocessing and Quality
AAIA Domain 3: AI-Assisted Audit Evidence Integrity

NEW QUESTION # 85
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?

A. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations.
B. It reduces the need for human oversight, ensuring seamless and autonomous data governance.
C. It focuses on enhancing the accuracy and reliability of AI model predictions.
D. It accelerates AI implementation timelines by fully automating data preparation processes.

Answer: A

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 # 86
Which of the following is the BEST way to support the development and design of high-risk AI systems?

A. Ensure the availability of trustworthy data sets.
B. Regularly back up the AI system's data to a secure, offsite location.
C. Conduct regular training sessions for users on data privacy.
D. Implement multi-factor authentication (MFA) for all users accessing the AI system.

Answer: A

NEW QUESTION # 87
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