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Isaca AAIA Practice Test 53

Question 520

An independent AI auditor reviews this case: An AI development team gives a shared service account authority to change training datasets and promote production weights. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  2. Inspect signed use cases, requirement trace matrix, outcome thresholds, known limitations and test cases.
  3. Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.
  4. Inventory and authenticate model components, third-party datasets and software dependencies throughout acquisition and updates.
  5. Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

Explanation

Correct Answers

C:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. This response fits the risk associated with AI data and model CI/CD separation: Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

E:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. For this situation: An AI development team gives a shared service account authority to change training datasets and promote production weights.

Incorrect Answers

A:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. Effective closure requires: Only signed approved model-data combinations reach production and privilege tests fail closed.

B:

This option concerns AI requirements-to-acceptance traceability, which is a related but different AI governance concern. It does not establish control effectiveness for AI data and model CI/CD separation.

D:

This option concerns AI vendor component and supply chain lifecycle, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

Question 521

A governance and assurance review uncovers the following: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Inventory and authenticate model components, third-party datasets and software dependencies throughout acquisition and updates.
  2. Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.
  3. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  4. Require risk-proportionate independent model validation, controlled challenge samples and release acceptance.

Explanation

Correct Answer

B:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. This is the proportionate control response to AI data and model CI/CD separation: Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

Incorrect Answers

A:

This option concerns AI vendor component and supply chain lifecycle, which is a related but different AI governance concern. The audit must corroborate: Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

C:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. In this case: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build.

D:

This option concerns Independent validation before model deployment, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

Question 522

A risk-based audit engagement examines this operating condition: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  2. A successful demo establishes feasibility, not sufficient reliability or authorization for real customer impact.
  3. AI supply-chain risk includes pre-trained weights, code, data sources and hosted capabilities.
  4. AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

Explanation

Correct Answer

D:

The acceptable verified outcome is: Only signed approved model-data combinations reach production and privilege tests fail closed. The corrective action is: Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

Incorrect Answers

A:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. For this situation: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build.

B:

This option concerns AI prototype experimentation versus production safety gates, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

C:

This option concerns AI vendor component and supply chain lifecycle, which is a related but different AI governance concern. For this situation: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build.

Question 523

An audit committee asks for a defensible assessment of this situation: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.
  2. Review reviewer independence, held-out data provenance, challenge results, exceptions and signoffs.
  3. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  4. Inspect signed use cases, requirement trace matrix, outcome thresholds, known limitations and test cases.

Explanation

Correct Answer

A:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. The corrective action is: Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

Incorrect Answers

B:

This option concerns Independent validation before model deployment, which is a related but different AI governance concern. For this situation: A CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build.

C:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

D:

This option concerns AI requirements-to-acceptance traceability, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

Question 524

During an AI controls assessment, the team learns that a CI pipeline pushes an unapproved model version directly to payroll decisions after any successful build. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  2. Approved model versions can be traced and materially reproduced with recorded dependencies.
  3. The retired model cannot serve unauthorized predictions and remaining data is governed by approved retention.
  4. Only signed approved model-data combinations reach production and privilege tests fail closed.

Explanation

Correct Answer

D:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. This is an observable validation result for AI data and model CI/CD separation, not just management assurance: Only signed approved model-data combinations reach production and privilege tests fail closed.

Incorrect Answers

A:

It misses the key control principle: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

B:

This option concerns Reproducible model training and configuration management, which is a related but different AI governance concern. The audit must corroborate: Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

C:

This option concerns Controlled model retirement and data disposition, which is a related but different AI governance concern. It does not establish control effectiveness for AI data and model CI/CD separation.

Question 525

An independent AI auditor reviews this case: An analyst notices staging feature pipelines using production patient data without masked test access rules. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Use a decommission plan to disable endpoints, revoke access, retain required records and dispose of data appropriately.
  2. Coordinate technical recovery with affected stakeholder review, corrective communication and validated post-incident controls.
  3. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  4. Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

Explanation

Correct Answer

D:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

Incorrect Answers

A:

This option concerns Controlled model retirement and data disposition, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

B:

This option concerns AI model rollback and stakeholder communication, which is a related but different AI governance concern. The decisive principle here is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.

C:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

Question 526

A governance and assurance review uncovers the following: An analyst notices staging feature pipelines using production patient data without masked test access rules. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes.
  2. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  3. AI development assurance starts from explicit risk-weighted requirements rather than retrospective claims of usefulness.
  4. Reproducibility requires more than saving an output weight file or project notebook.

Explanation

Correct Answer

A:

The acceptable verified outcome is: Only signed approved model-data combinations reach production and privilege tests fail closed. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

Incorrect Answers

B:

This attractive shortcut does not resolve the AI data and model CI/CD separation weakness: Allow every data scientist to alter training inputs and production model endpoints without change approval. For this situation: An analyst notices staging feature pipelines using production patient data without masked test access rules.

C:

This option concerns AI requirements-to-acceptance traceability, which is a related but different AI governance concern. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

D:

This option concerns Reproducible model training and configuration management, which is a related but different AI governance concern. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

Question 527

A risk-based audit engagement examines this operating condition: An analyst notices staging feature pipelines using production patient data without masked test access rules. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.
  2. Allow every data scientist to alter training inputs and production model endpoints without change approval.
  3. Inspect image dependencies, seeds, pipelines, registry signatures, lineage and controlled reruns.
  4. Inspect signed use cases, requirement trace matrix, outcome thresholds, known limitations and test cases.

Explanation

Correct Answer

A:

The reason it is necessary is: AI delivery involves models, data and code, so conventional app deployment approval alone may not cover behavior changes. Independent audit evidence includes: Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

Incorrect Answers

B:

For this situation: An analyst notices staging feature pipelines using production patient data without masked test access rules.

C:

This option concerns Reproducible model training and configuration management, which is a related but different AI governance concern. For this situation: An analyst notices staging feature pipelines using production patient data without masked test access rules.

D:

This option concerns AI requirements-to-acceptance traceability, which is a related but different AI governance concern. For this situation: An analyst notices staging feature pipelines using production patient data without masked test access rules.

Question 528

An audit committee asks for a defensible assessment of this situation: An analyst notices staging feature pipelines using production patient data without masked test access rules. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Production release occurs only after required controls and independent signoffs are demonstrated.
  2. Each material requirement maps to a result and signoff before deploying the feature.
  3. Only signed approved model-data combinations reach production and privilege tests fail closed.
  4. Allow every data scientist to alter training inputs and production model endpoints without change approval.

Explanation

Correct Answer

C:

This is an observable validation result for AI data and model CI/CD separation, not just management assurance: Only signed approved model-data combinations reach production and privilege tests fail closed.

Incorrect Answers

A:

This option concerns AI prototype experimentation versus production safety gates, which is a related but different AI governance concern. In this case: An analyst notices staging feature pipelines using production patient data without masked test access rules.

B:

This option concerns AI requirements-to-acceptance traceability, which is a related but different AI governance concern. The audit must corroborate: Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

D:

The auditor should instead examine: Inspect pipeline roles, artifact signatures, dataset permissions, approval gates and production change logs.

Question 529

During an AI controls assessment, the team learns that a company downloads an open-source AI model checkpoint from an unverified mirror without provenance. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Inventory and authenticate model components, third-party datasets and software dependencies throughout acquisition and updates.
  2. Require risk-proportionate independent model validation, controlled challenge samples and release acceptance.
  3. Assume model weights and datasets require no provenance controls because they are not executable source code.
  4. Separate sandbox experimentation from production through constrained access, risk approval and evidence-based readiness criteria.

Explanation

Correct Answer

A:

The reason it is necessary is: AI supply-chain risk includes pre-trained weights, code, data sources and hosted capabilities. The acceptable verified outcome is: Only approved traceable versions run and vulnerable components receive monitored remediation.

Incorrect Answers

B:

This option concerns Independent validation before model deployment, which is a related but different AI governance concern. It does not establish control effectiveness for AI vendor component and supply chain lifecycle.

C:

This attractive shortcut does not resolve the AI vendor component and supply chain lifecycle weakness: Assume model weights and datasets require no provenance controls because they are not executable source code.

D:

This option concerns AI prototype experimentation versus production safety gates, which is a related but different AI governance concern. It does not establish control effectiveness for AI vendor component and supply chain lifecycle.

Question 530

An independent AI auditor reviews this case: A company downloads an open-source AI model checkpoint from an unverified mirror without provenance. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Review endpoint inventory, credentials, retained artifacts, disposal approvals, legal holds and restoration paths.
  2. Inventory and authenticate model components, third-party datasets and software dependencies throughout acquisition and updates.
  3. Assume model weights and datasets require no provenance controls because they are not executable source code.
  4. Inspect supplier attestations, component inventory, signed digests, license constraints, SBOM and update notices.
  5. Separate development, test and production roles with controlled promotion, scoped credentials and audit trails.

Explanation

Correct Answers

B:

The reason it is necessary is: AI supply-chain risk includes pre-trained weights, code, data sources and hosted capabilities. For this situation: A company downloads an open-source AI model checkpoint from an unverified mirror without provenance.

D:

The reason it is necessary is: AI supply-chain risk includes pre-trained weights, code, data sources and hosted capabilities. In this case: A company downloads an open-source AI model checkpoint from an unverified mirror without provenance.

Incorrect Answers

A:

This option concerns Controlled model retirement and data disposition, which is a related but different AI governance concern. It does not establish control effectiveness for AI vendor component and supply chain lifecycle.

C:

This attractive shortcut does not resolve the AI vendor component and supply chain lifecycle weakness: Assume model weights and datasets require no provenance controls because they are not executable source code. For this situation: A company downloads an open-source AI model checkpoint from an unverified mirror without provenance.

E:

This option concerns AI data and model CI/CD separation, which is a related but different AI governance concern. It does not establish control effectiveness for AI vendor component and supply chain lifecycle.

ISACA Advanced in AI Audit (AAIA) Practice Tests