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Isaca AAIA Practice Test 67

Question 660

An independent AI auditor reviews this case: An insurance chatbot's policy answer is challenged but logs omit the retrieval sources and prompt configuration. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Capture necessary model version, inputs or safe references, key context, outcome and human intervention metadata.
  2. Inspect logging schema, privacy redaction, identifiers, time ordering, retention and replay tests.
  3. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.
  4. Store only aggregate daily counts and claim every individual AI decision remains fully explainable.
  5. Inspect calibration, confusion matrices, missing-input behavior, escalation queue and approval history.

Explanation

Correct Answers

A:

The reason it is necessary is: Auditability of an AI decision depends on traceable context, not just a final predicted label. For this situation: An insurance chatbot's policy answer is challenged but logs omit the retrieval sources and prompt configuration.

B:

This evidence directly tests the asserted control for AI decision logs and explanation traceability: Inspect logging schema, privacy redaction, identifiers, time ordering, retention and replay tests.

Incorrect Answers

C:

This option concerns Feedback-loop contamination from user corrections, which is a related but different AI governance concern. In this case: An insurance chatbot's policy answer is challenged but logs omit the retrieval sources and prompt configuration.

D:

It misses the key control principle: Auditability of an AI decision depends on traceable context, not just a final predicted label.

E:

This option concerns Threshold governance and safety fallbacks, which is a related but different AI governance concern. The outcome to verify is: An authorized reviewer can reconstruct a sampled decision without overcollecting personal data.

Question 661

A governance and assurance review uncovers the following: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Define owner-led ongoing monitoring for quality, drift, safety, fairness and service impact with escalation.
  2. Capture necessary model version, inputs or safe references, key context, outcome and human intervention metadata.
  3. Treat every user click or thumbs-up as an authoritative label and retrain automatically.
  4. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.

Explanation

Correct Answer

D:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. The acceptable verified outcome is: Approved feedback improves measured outcomes without adversarial self-reinforcing error.

Incorrect Answers

A:

This option concerns AI post-deployment monitoring and KRI ownership, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

B:

This option concerns AI decision logs and explanation traceability, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

C:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Question 662

A risk-based audit engagement examines this operating condition: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Autonomous action risk is driven by actual tool permissions and decision gates, not chatbot prompt wording alone.
  2. Treat every user click or thumbs-up as an authoritative label and retrain automatically.
  3. Feedback improves models only when its quality and susceptibility to manipulation are controlled.
  4. Benchmark scores can overstate generalization when test items or related records have been exposed to development.

Explanation

Correct Answer

C:

Independent audit evidence includes: Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback. This is the applicable AI audit principle for Feedback-loop contamination from user corrections: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

Incorrect Answers

A:

This option concerns Autonomous agent action-limit supervision, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

B:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. For this situation: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review.

D:

This option concerns Training and evaluation benchmark contamination, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

Question 663

An audit committee asks for a defensible assessment of this situation: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect service identities, tool-call logs, policy checks, denial tests and human approval traces.
  2. Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.
  3. Inspect reviewer workload, UI, explanations, override log and interviews on actual cases.
  4. Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Explanation

Correct Answer

B:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. In this case: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review.

Incorrect Answers

A:

This option concerns Autonomous agent action-limit supervision, which is a related but different AI governance concern. For this situation: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review.

C:

This option concerns Effective human oversight of AI recommendations, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

D:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. The auditor should instead examine: Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.

Question 664

During an AI controls assessment, the team learns that a recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Reviewers correctly reject unsafe recommendations and escalation works within operational time constraints.
  2. Threshold breaches lead to timely investigation and documented changes to protect affected users.
  3. Approved feedback improves measured outcomes without adversarial self-reinforcing error.
  4. Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Explanation

Correct Answer

C:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. This is an observable validation result for Feedback-loop contamination from user corrections, not just management assurance: Approved feedback improves measured outcomes without adversarial self-reinforcing error.

Incorrect Answers

A:

This option concerns Effective human oversight of AI recommendations, which is a related but different AI governance concern. For this situation: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review.

B:

This option concerns AI post-deployment monitoring and KRI ownership, which is a related but different AI governance concern. For this situation: A recommendation model retrains automatically on user clicks, amplifying manipulated click farms without review.

D:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. Effective closure requires: Approved feedback improves measured outcomes without adversarial self-reinforcing error.

Question 665

An independent AI auditor reviews this case: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.
  2. Treat every user click or thumbs-up as an authoritative label and retrain automatically.
  3. Define calibrated thresholds and safe defer-to-human or rule-based fallbacks for uncertain or missing-data conditions.
  4. Provide reviewers timely context, override authority, training and auditable independent decision space.

Explanation

Correct Answer

A:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. Independent audit evidence includes: Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.

Incorrect Answers

B:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. In this case: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses.

C:

This option concerns Threshold governance and safety fallbacks, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

D:

This option concerns Effective human oversight of AI recommendations, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

Question 666

A governance and assurance review uncovers the following: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Feedback improves models only when its quality and susceptibility to manipulation are controlled.
  2. Autonomous action risk is driven by actual tool permissions and decision gates, not chatbot prompt wording alone.
  3. Human-in-the-loop labeling does not create real oversight when reviewers cannot understand or influence outcomes.
  4. Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Explanation

Correct Answer

A:

Independent audit evidence includes: Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback. The acceptable verified outcome is: Approved feedback improves measured outcomes without adversarial self-reinforcing error.

Incorrect Answers

B:

This option concerns Autonomous agent action-limit supervision, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

C:

This option concerns Effective human oversight of AI recommendations, which is a related but different AI governance concern. For this situation: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses.

D:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. For this situation: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses.

Question 667

A risk-based audit engagement examines this operating condition: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect logging schema, privacy redaction, identifiers, time ordering, retention and replay tests.
  2. Treat every user click or thumbs-up as an authoritative label and retrain automatically.
  3. Review appeal turnaround, reviewer authority, data correction process, overturned cases and notices.
  4. Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.

Explanation

Correct Answer

D:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. This evidence directly tests the asserted control for Feedback-loop contamination from user corrections: Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.

Incorrect Answers

A:

This option concerns AI decision logs and explanation traceability, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

B:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.

C:

This option concerns Customer appeal and remedy for automated outcomes, which is a related but different AI governance concern. It does not establish control effectiveness for Feedback-loop contamination from user corrections.

Question 668

An audit committee asks for a defensible assessment of this situation: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Approved feedback improves measured outcomes without adversarial self-reinforcing error.
  2. Uncertain cases route safely and measured errors remain within approved operational and fairness limits.
  3. An authorized reviewer can reconstruct a sampled decision without overcollecting personal data.
  4. Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Explanation

Correct Answer

A:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. The corrective action is: Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.

Incorrect Answers

B:

This option concerns Threshold governance and safety fallbacks, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

C:

This option concerns AI decision logs and explanation traceability, which is a related but different AI governance concern. For this situation: A customer chatbot marks all thumbs-up feedback as ground truth including abusive attempts to steer responses.

D:

It misses the key control principle: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

Question 669

During an AI controls assessment, the team learns that a medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Design actionable challenge, human reassessment, factual correction and remediation for adverse AI decisions.
  2. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.
  3. Treat every user click or thumbs-up as an authoritative label and retrain automatically.
  4. Define owner-led ongoing monitoring for quality, drift, safety, fairness and service impact with escalation.

Explanation

Correct Answer

B:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. In this case: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

Incorrect Answers

A:

This option concerns Customer appeal and remedy for automated outcomes, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

C:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. In this case: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

D:

This option concerns AI post-deployment monitoring and KRI ownership, which is a related but different AI governance concern. The decisive principle here is: Feedback improves models only when its quality and susceptibility to manipulation are controlled.

Question 670

An independent AI auditor reviews this case: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Inspect feedback source identities, adjudication rates, unusual bursts, retraining eligibility and rollback.
  2. Verify feedback provenance, reviewer authority and sampling before incorporating live corrections into future models.
  3. Define owner-led ongoing monitoring for quality, drift, safety, fairness and service impact with escalation.
  4. Review appeal turnaround, reviewer authority, data correction process, overturned cases and notices.
  5. Treat every user click or thumbs-up as an authoritative label and retrain automatically.

Explanation

Correct Answers

A:

The acceptable verified outcome is: Approved feedback improves measured outcomes without adversarial self-reinforcing error.

B:

The reason it is necessary is: Feedback improves models only when its quality and susceptibility to manipulation are controlled. For this situation: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

Incorrect Answers

C:

This option concerns AI post-deployment monitoring and KRI ownership, which is a related but different AI governance concern. For this situation: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

D:

This option concerns Customer appeal and remedy for automated outcomes, which is a related but different AI governance concern. For this situation: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

E:

This attractive shortcut does not resolve the Feedback-loop contamination from user corrections weakness: Treat every user click or thumbs-up as an authoritative label and retrain automatically. For this situation: A medical classifier ingests clinician corrections that contain unresolved disagreements as verified labels.

ISACA Advanced in AI Audit (AAIA) Practice Tests