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Isaca AAIA Practice Test 28

Question 270

An independent AI auditor reviews this case: A claims bot includes identity-card images in context even when only policy number is required. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Minimize exposed attributes, redact unnecessary personal data and enforce purpose-bound access before model calls.
  2. Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers.
  3. Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.
  4. Send all available sensitive context to improve answer quality without measuring necessity.
  5. Validate intended secondary use against lawful basis, original purposes and documented governance before training.

Explanation

Correct Answers

A:

The reason it is necessary is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages. For this situation: A claims bot includes identity-card images in context even when only policy number is required.

C:

The reason it is necessary is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages. This evidence directly tests the asserted control for Data minimization and PII in prompts: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

Incorrect Answers

B:

This option concerns AI inference retention and right-to-erasure handling, which is a related but different AI governance concern. For this situation: A claims bot includes identity-card images in context even when only policy number is required.

D:

This attractive shortcut does not resolve the Data minimization and PII in prompts weakness: Send all available sensitive context to improve answer quality without measuring necessity. For this situation: A claims bot includes identity-card images in context even when only policy number is required.

E:

This option concerns Purpose limitation in AI training data reuse, which is a related but different AI governance concern. For this situation: A claims bot includes identity-card images in context even when only policy number is required.

Question 271

A governance and assurance review uncovers the following: A claims bot includes identity-card images in context even when only policy number is required. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Compare feature necessity, identifiability, model utility, transformation design and data retention.
  2. Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.
  3. Send all available sensitive context to improve answer quality without measuring necessity.
  4. Compare use-case requirements, deterministic baselines, quantitative error rates, grounded outputs and exception handling.

Explanation

Correct Answer

B:

The reason it is necessary is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages. The corrective action is: Minimize exposed attributes, redact unnecessary personal data and enforce purpose-bound access before model calls.

Incorrect Answers

A:

This option concerns Privacy by design for model features, which is a related but different AI governance concern. It does not establish control effectiveness for Data minimization and PII in prompts.

C:

This attractive shortcut does not resolve the Data minimization and PII in prompts weakness: Send all available sensitive context to improve answer quality without measuring necessity. Minimize exposed attributes, redact unnecessary personal data and enforce purpose-bound access before model calls.

D:

This option concerns Predictive versus generative model task fit, which is a related but different AI governance concern. The decisive principle here is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages.

Question 272

A risk-based audit engagement examines this operating condition: A claims bot includes identity-card images in context even when only policy number is required. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Send all available sensitive context to improve answer quality without measuring necessity.
  2. Deletion requests remove or appropriately constrain applicable copies within documented policy and legal requirements.
  3. Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.
  4. Only permitted records enter the use case, with documented restrictions and review of new purposes.

Explanation

Correct Answer

C:

The reason it is necessary is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages. Independent audit evidence includes: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

Incorrect Answers

A:

For this situation: A claims bot includes identity-card images in context even when only policy number is required.

B:

This option concerns AI inference retention and right-to-erasure handling, which is a related but different AI governance concern. The audit must corroborate: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

D:

This option concerns Purpose limitation in AI training data reuse, which is a related but different AI governance concern. The audit must corroborate: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

Question 273

An audit committee asks for a defensible assessment of this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Minimize exposed attributes, redact unnecessary personal data and enforce purpose-bound access before model calls.
  2. Match the model family to the task, retain deterministic calculations where appropriate and validate generative outputs against trusted data and human review.
  3. Send all available sensitive context to improve answer quality without measuring necessity.
  4. Test whether less identifying features or on-premises transformations satisfy the business purpose.

Explanation

Correct Answer

A:

The reason it is necessary is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages. In this case: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

Incorrect Answers

B:

This option concerns Predictive versus generative model task fit, which is a related but different AI governance concern. The audit must corroborate: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

C:

This attractive shortcut does not resolve the Data minimization and PII in prompts weakness: Send all available sensitive context to improve answer quality without measuring necessity. In this case: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

D:

This option concerns Privacy by design for model features, which is a related but different AI governance concern. The decisive principle here is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages.

Question 274

During an AI controls assessment, the team learns that a service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Send all available sensitive context to improve answer quality without measuring necessity.
  2. Generative fluency and predictive statistical accuracy address different tasks; auditors must test fitness for a defined business decision.
  3. Model suitability depends on performance, explainability and consequences of errors; a marginal metric gain does not remove oversight obligations.
  4. Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages.

Explanation

Correct Answer

D:

The acceptable verified outcome is: Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests. Independent audit evidence includes: Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

Incorrect Answers

A:

This attractive shortcut does not resolve the Data minimization and PII in prompts weakness: Send all available sensitive context to improve answer quality without measuring necessity. For this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

B:

This option concerns Predictive versus generative model task fit, which is a related but different AI governance concern. For this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

C:

This option concerns Explainable credit-model selection for adverse decisions, which is a related but different AI governance concern. The decisive principle here is: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages.

Question 275

An independent AI auditor reviews this case: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.
  2. Send all available sensitive context to improve answer quality without measuring necessity.
  3. Compare feature necessity, identifiability, model utility, transformation design and data retention.
  4. Inspect use-case criticality, model comparison, reason-code fidelity, feature lineage, adverse-decision samples and appeal test results.

Explanation

Correct Answer

A:

The acceptable verified outcome is: Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.

Incorrect Answers

B:

It misses the key control principle: Data minimization reduces privacy exposure at input, storage, retrieval and telemetry stages.

C:

This option concerns Privacy by design for model features, which is a related but different AI governance concern. For this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

D:

This option concerns Explainable credit-model selection for adverse decisions, which is a related but different AI governance concern. For this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

Question 276

A governance and assurance review uncovers the following: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization. After management addresses the issue, which result MOST convincingly demonstrates effective remediation? Select ONE best answer.

  1. Selected components meet task-specific accuracy, controllability and auditability tests on representative work.
  2. Only permitted records enter the use case, with documented restrictions and review of new purposes.
  3. Send all available sensitive context to improve answer quality without measuring necessity.
  4. Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.

Explanation

Correct Answer

D:

This is an observable validation result for Data minimization and PII in prompts, not just management assurance: Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.

Incorrect Answers

A:

This option concerns Predictive versus generative model task fit, which is a related but different AI governance concern. The outcome to verify is: Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.

B:

This option concerns Purpose limitation in AI training data reuse, which is a related but different AI governance concern. The outcome to verify is: Normal tasks succeed with reduced personal information and prohibited fields do not enter model requests.

C:

For this situation: A service desk embeds full chat transcripts with phone numbers into a model for simple ticket categorization.

Question 277

A risk-based audit engagement examines this operating condition: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely. What is the MOST appropriate risk-based audit recommendation that addresses the root control weakness? Select ONE best answer.

  1. Treat disabling future training as equivalent to deleting all already-retained personal data.
  2. Validate intended secondary use against lawful basis, original purposes and documented governance before training.
  3. Define defensible retention, deletion and downstream propagation processes with technical evidence of execution.
  4. Require decision-level explanations proportionate to the use case, validated predictive benefit, documented limitations and a meaningful appeal path.

Explanation

Correct Answer

C:

The reason it is necessary is: Contractual no-training promises do not address independent log retention, indexing or deletion obligations. In this case: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely.

Incorrect Answers

A:

This attractive shortcut does not resolve the AI inference retention and right-to-erasure handling weakness: Treat disabling future training as equivalent to deleting all already-retained personal data.

B:

This option concerns Purpose limitation in AI training data reuse, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

D:

This option concerns Explainable credit-model selection for adverse decisions, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

Question 278

An audit committee asks for a defensible assessment of this situation: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely. Which principle BEST explains why the observed practice is a material governance or control concern? Select ONE best answer.

  1. Contractual no-training promises do not address independent log retention, indexing or deletion obligations.
  2. Generative fluency and predictive statistical accuracy address different tasks; auditors must test fitness for a defined business decision.
  3. Privacy by design changes processing architecture rather than relying solely on downstream access policy.
  4. Treat disabling future training as equivalent to deleting all already-retained personal data.

Explanation

Correct Answer

A:

Independent audit evidence includes: Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers. This is the applicable AI audit principle for AI inference retention and right-to-erasure handling: Contractual no-training promises do not address independent log retention, indexing or deletion obligations.

Incorrect Answers

B:

This option concerns Predictive versus generative model task fit, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

C:

This option concerns Privacy by design for model features, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

D:

This attractive shortcut does not resolve the AI inference retention and right-to-erasure handling weakness: Treat disabling future training as equivalent to deleting all already-retained personal data. For this situation: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely.

Question 279

During an AI controls assessment, the team learns that a chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely. What evidence should the auditor examine FIRST to substantiate or challenge the control owner’s claim? Select ONE best answer.

  1. Inspect region endpoints, service architecture, contractual transfer mechanisms, subprocessor lists and logs.
  2. Treat disabling future training as equivalent to deleting all already-retained personal data.
  3. Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers.
  4. Inspect data flows, prompt logs, retrieval chunks, masking controls and representative least-data tests.

Explanation

Correct Answer

C:

The reason it is necessary is: Contractual no-training promises do not address independent log retention, indexing or deletion obligations. This evidence directly tests the asserted control for AI inference retention and right-to-erasure handling: Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers.

Incorrect Answers

A:

This option concerns Cross-border AI data transfer mapping, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

B:

This attractive shortcut does not resolve the AI inference retention and right-to-erasure handling weakness: Treat disabling future training as equivalent to deleting all already-retained personal data. The auditor should instead examine: Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers.

D:

This option concerns Data minimization and PII in prompts, which is a related but different AI governance concern. It does not establish control effectiveness for AI inference retention and right-to-erasure handling.

Question 280

An independent AI auditor reviews this case: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely. Which TWO complementary elements should the auditor seek: an effective corrective control and the most probative independent evidence? Select TWO answers.

  1. Treat disabling future training as equivalent to deleting all already-retained personal data.
  2. Inspect use-case criticality, model comparison, reason-code fidelity, feature lineage, adverse-decision samples and appeal test results.
  3. Inspect provider terms, retention schedules, index lifecycle, backups, deletion tickets and test identifiers.
  4. Define defensible retention, deletion and downstream propagation processes with technical evidence of execution.
  5. Minimize exposed attributes, redact unnecessary personal data and enforce purpose-bound access before model calls.

Explanation

Correct Answers

C:

The reason it is necessary is: Contractual no-training promises do not address independent log retention, indexing or deletion obligations. For this situation: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely.

D:

The reason it is necessary is: Contractual no-training promises do not address independent log retention, indexing or deletion obligations. This response fits the risk associated with AI inference retention and right-to-erasure handling: Define defensible retention, deletion and downstream propagation processes with technical evidence of execution.

Incorrect Answers

A:

This attractive shortcut does not resolve the AI inference retention and right-to-erasure handling weakness: Treat disabling future training as equivalent to deleting all already-retained personal data. Effective closure requires: Deletion requests remove or appropriately constrain applicable copies within documented policy and legal requirements.

B:

This option concerns Explainable credit-model selection for adverse decisions, which is a related but different AI governance concern. For this situation: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely.

E:

This option concerns Data minimization and PII in prompts, which is a related but different AI governance concern. For this situation: A chatbot provider promises no training on client prompts but retains full identifiable transcripts indefinitely.

ISACA Advanced in AI Audit (AAIA) Practice Tests