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Isaca AAIA Practice Test 112

Question 1110

An independent AI auditor reviews this case: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.
  2. Validate audit-tool outputs against independent truth, monitor drift and use human judgment for material findings.
  3. Reconcile AI inventory, data flows, business ownership, complaints, material incidents and risk appetite to audit plan.
  4. Treat a large CSV row count as proof of complete AI decision coverage.
  5. Reconcile source populations, failed records, joins and transformations before interpreting AI audit analytics.

Explanation

Correct Answers

A:

The reason it is necessary is: Incomplete populations and silent joins can bias control effectiveness conclusions. In this case: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

E:

The reason it is necessary is: Incomplete populations and silent joins can bias control effectiveness conclusions. For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

Incorrect Answers

B:

This option concerns Validation of AI-enabled audit analytics tools, which is a related but different AI governance concern. The decisive principle here is: Incomplete populations and silent joins can bias control effectiveness conclusions.

C:

This option concerns Risk-based AI audit universe and scope, which is a related but different AI governance concern. For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

D:

This attractive shortcut does not resolve the Audit data completeness and reconciliation weakness: Treat a large CSV row count as proof of complete AI decision coverage. For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

Question 1111

A governance and assurance review uncovers the following: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Review challenge samples, model changes, error rates, reviewer overrides and data privacy restrictions.
  2. Inspect source test inputs, environment version, expected result, output logs and exception handling.
  3. Treat a large CSV row count as proof of complete AI decision coverage.
  4. Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

Explanation

Correct Answer

D:

The reason it is necessary is: Incomplete populations and silent joins can bias control effectiveness conclusions. This evidence directly tests the asserted control for Audit data completeness and reconciliation: Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

Incorrect Answers

A:

This option concerns Validation of AI-enabled audit analytics tools, which is a related but different AI governance concern. For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

B:

This option concerns Reperformance and adversarial audit test cases, which is a related but different AI governance concern. The decisive principle here is: Incomplete populations and silent joins can bias control effectiveness conclusions.

C:

This attractive shortcut does not resolve the Audit data completeness and reconciliation weakness: Treat a large CSV row count as proof of complete AI decision coverage. Effective closure requires: End-to-end reconciliations explain missing and duplicated records before sampling and metrics.

Question 1112

A risk-based audit engagement examines this operating condition: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Audit analytics identify defensible exceptions while avoiding unsupported causal and discriminatory conclusions.
  2. Treat a large CSV row count as proof of complete AI decision coverage.
  3. Workpapers map each issue to an explicit criterion, condition, cause, consequence and evidence.
  4. End-to-end reconciliations explain missing and duplicated records before sampling and metrics.

Explanation

Correct Answer

D:

The reason it is necessary is: Incomplete populations and silent joins can bias control effectiveness conclusions. Independent audit evidence includes: Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

Incorrect Answers

A:

This option concerns Audit analytics drift and anomalous trend interpretation, which is a related but different AI governance concern. It does not establish control effectiveness for Audit data completeness and reconciliation.

B:

For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

C:

This option concerns AI audit criteria and control objectives, which is a related but different AI governance concern. For this situation: A model monitoring export reports only successful requests while the highest-risk failed decisions are absent.

Question 1113

An audit committee asks for a defensible assessment of this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Reconcile source populations, failed records, joins and transformations before interpreting AI audit analytics.
  2. Treat a large CSV row count as proof of complete AI decision coverage.
  3. Define objective risk-based audit criteria from approved organizational policies, relevant standards and applicable law.
  4. Use context-aware segment and time analysis with corroboration, uncertainty and appropriate baseline comparisons.

Explanation

Correct Answer

A:

The reason it is necessary is: Incomplete populations and silent joins can bias control effectiveness conclusions. In this case: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

Incorrect Answers

B:

This attractive shortcut does not resolve the Audit data completeness and reconciliation weakness: Treat a large CSV row count as proof of complete AI decision coverage. In this case: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

C:

This option concerns AI audit criteria and control objectives, which is a related but different AI governance concern. The audit must corroborate: Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

D:

This option concerns Audit analytics drift and anomalous trend interpretation, which is a related but different AI governance concern. The decisive principle here is: Incomplete populations and silent joins can bias control effectiveness conclusions.

Question 1114

During an AI controls assessment, the team learns that an audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. AI in the audit process introduces its own reliability and confidentiality risks that must be controlled.
  2. Incomplete populations and silent joins can bias control effectiveness conclusions.
  3. Treat a large CSV row count as proof of complete AI decision coverage.
  4. AI-assisted audit planning can improve coverage but retains risks of confidentiality, bias and fabricated conclusions.

Explanation

Correct Answer

B:

The corrective action is: Reconcile source populations, failed records, joins and transformations before interpreting AI audit analytics. The acceptable verified outcome is: End-to-end reconciliations explain missing and duplicated records before sampling and metrics.

Incorrect Answers

A:

This option concerns Validation of AI-enabled audit analytics tools, which is a related but different AI governance concern. The audit must corroborate: Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

C:

This attractive shortcut does not resolve the Audit data completeness and reconciliation weakness: Treat a large CSV row count as proof of complete AI decision coverage. For this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

D:

This option concerns Using AI during audit engagement planning, which is a related but different AI governance concern. The decisive principle here is: Incomplete populations and silent joins can bias control effectiveness conclusions.

Question 1115

An independent AI auditor reviews this case: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.
  2. Treat a large CSV row count as proof of complete AI decision coverage.
  3. Inspect audit program, control objectives, regulatory applicability, measurements and management agreement on criteria.
  4. Inspect seasonality, cohort mix, outliers, confidence intervals, drift metrics and independent transactions.

Explanation

Correct Answer

A:

The corrective action is: Reconcile source populations, failed records, joins and transformations before interpreting AI audit analytics.

Incorrect Answers

B:

It misses the key control principle: Incomplete populations and silent joins can bias control effectiveness conclusions.

C:

This option concerns AI audit criteria and control objectives, which is a related but different AI governance concern. For this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

D:

This option concerns Audit analytics drift and anomalous trend interpretation, which is a related but different AI governance concern. For this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

Question 1116

A governance and assurance review uncovers the following: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Treat a large CSV row count as proof of complete AI decision coverage.
  2. End-to-end reconciliations explain missing and duplicated records before sampling and metrics.
  3. The plan covers material high-risk deployments and states documented scope boundaries and assurance limitations.
  4. An accountable auditor validates the final scope and retains evidence for AI-generated suggestions.

Explanation

Correct Answer

B:

This is an observable validation result for Audit data completeness and reconciliation, not just management assurance: End-to-end reconciliations explain missing and duplicated records before sampling and metrics.

Incorrect Answers

A:

For this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

C:

This option concerns Risk-based AI audit universe and scope, which is a related but different AI governance concern. The audit must corroborate: Inspect row counts, independent totals, missing keys, excluded statuses and transformation logs.

D:

This option concerns Using AI during audit engagement planning, which is a related but different AI governance concern. For this situation: An audit analytics script joins predictions to customer outcomes and silently drops unmatched identifiers.

Question 1117

A risk-based audit engagement examines this operating condition: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Define objective risk-based audit criteria from approved organizational policies, relevant standards and applicable law.
  2. Use context-aware segment and time analysis with corroboration, uncertainty and appropriate baseline comparisons.
  3. Reperform representative positive and negative controls using independently selected inputs and authorized challenge procedures.
  4. Convert any unusual dashboard trend directly into a confirmed AI control failure finding.

Explanation

Correct Answer

B:

The reason it is necessary is: An anomaly is a hypothesis requiring data quality and causal investigation, not automatic proof of a control failure. The acceptable verified outcome is: Audit analytics identify defensible exceptions while avoiding unsupported causal and discriminatory conclusions.

Incorrect Answers

A:

This option concerns AI audit criteria and control objectives, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

C:

This option concerns Reperformance and adversarial audit test cases, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

D:

This attractive shortcut does not resolve the Audit analytics drift and anomalous trend interpretation weakness: Convert any unusual dashboard trend directly into a confirmed AI control failure finding.

Question 1118

An audit committee asks for a defensible assessment of this situation: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Convert any unusual dashboard trend directly into a confirmed AI control failure finding.
  2. Audit scope must follow enterprise AI exposure, not only visible software projects or infrastructure boundaries.
  3. An anomaly is a hypothesis requiring data quality and causal investigation, not automatic proof of a control failure.
  4. AI-assisted audit planning can improve coverage but retains risks of confidentiality, bias and fabricated conclusions.

Explanation

Correct Answer

C:

The acceptable verified outcome is: Audit analytics identify defensible exceptions while avoiding unsupported causal and discriminatory conclusions. In this case: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

Incorrect Answers

A:

This attractive shortcut does not resolve the Audit analytics drift and anomalous trend interpretation weakness: Convert any unusual dashboard trend directly into a confirmed AI control failure finding. For this situation: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

B:

This option concerns Risk-based AI audit universe and scope, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

D:

This option concerns Using AI during audit engagement planning, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

Question 1119

During an AI controls assessment, the team learns that an auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Convert any unusual dashboard trend directly into a confirmed AI control failure finding.
  2. Inspect seasonality, cohort mix, outliers, confidence intervals, drift metrics and independent transactions.
  3. Inspect source test inputs, environment version, expected result, output logs and exception handling.
  4. Inspect query code, data hashes, metric definitions, source versions, filters and rerun evidence.

Explanation

Correct Answer

B:

The reason it is necessary is: An anomaly is a hypothesis requiring data quality and causal investigation, not automatic proof of a control failure. In this case: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

Incorrect Answers

A:

This attractive shortcut does not resolve the Audit analytics drift and anomalous trend interpretation weakness: Convert any unusual dashboard trend directly into a confirmed AI control failure finding. The auditor should instead examine: Inspect seasonality, cohort mix, outliers, confidence intervals, drift metrics and independent transactions.

C:

This option concerns Reperformance and adversarial audit test cases, which is a related but different AI governance concern. For this situation: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

D:

This option concerns Reproducible audit data transformations, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

Question 1120

An independent AI auditor reviews this case: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Convert any unusual dashboard trend directly into a confirmed AI control failure finding.
  2. Inspect seasonality, cohort mix, outliers, confidence intervals, drift metrics and independent transactions.
  3. Define the AI audit universe and scope using use-case impact, third parties, control reliance and key risk scenarios.
  4. Review challenge samples, model changes, error rates, reviewer overrides and data privacy restrictions.
  5. Use context-aware segment and time analysis with corroboration, uncertainty and appropriate baseline comparisons.

Explanation

Correct Answers

B:

The reason it is necessary is: An anomaly is a hypothesis requiring data quality and causal investigation, not automatic proof of a control failure. For this situation: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

E:

This response fits the risk associated with Audit analytics drift and anomalous trend interpretation: Use context-aware segment and time analysis with corroboration, uncertainty and appropriate baseline comparisons.

Incorrect Answers

A:

This attractive shortcut does not resolve the Audit analytics drift and anomalous trend interpretation weakness: Convert any unusual dashboard trend directly into a confirmed AI control failure finding. Effective closure requires: Audit analytics identify defensible exceptions while avoiding unsupported causal and discriminatory conclusions.

C:

This option concerns Risk-based AI audit universe and scope, which is a related but different AI governance concern. For this situation: An auditor flags every spike in model-denial rate as misconduct without adjusting for changing application volumes.

D:

This option concerns Validation of AI-enabled audit analytics tools, which is a related but different AI governance concern. It does not establish control effectiveness for Audit analytics drift and anomalous trend interpretation.

ISACA Advanced in AI Audit (AAIA) Practice Tests