AAIA Practice Test

AAIA Practice Test practice questions, page 16 of 23, with answer review and explanations.

Question 76

An organization has deployed a generative AI system for customer support that includes frequent updates to the AI model after deployment. Which of the following represents the GREATEST risk?

  1. Lack of a change management policy specific to AI
  2. Overreliance on manual review of AI model outputs
  3. Lack of continuous monitoring for model changes
  4. Lack of a dedicated AI governance committee

Question 77

An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?

  1. Deficiencies in policies and procedures validating AI model accuracy
  2. Limited documentation of user access permissions
  3. Excessive dependence on automated data collection and cleansing
  4. Gaps in AI privacy compliance and accountability

Question 78

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?

  1. Unfair loan decisions
  2. Delays in loan approval
  3. Reduced customer satisfaction
  4. Increased manual processing of applications

Question 79

Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?

  1. To align with global AI data privacy standards
  2. To mitigate reputational risk associated with public scrutiny of AI systems
  3. To ensure ethical integrity throughout the AI life cycle
  4. To establish guardrails limiting AI system functionality to approved use cases

Question 80

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?

  1. Failure to identify anomalies that can bias training data
  2. Lack of regular quality reviews for training data
  3. Infrequent updates to anomaly detection algorithms
  4. Inadequate staff training on the use of the system