Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 41:

  Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?

  A. Creating a centralized team to oversee the classification of data used in AI projects
  B. Conducting quarterly audits of AI data sets for anomalies and missing metadata
  C. Establishing a manual process to categorize data based on business needs and regulatory compliance
  D. Implementing an automated data cataloging tool that integrates with all organizational data repositories
  D. Implementing an automated data cataloging tool that integrates with all organizational data repositories

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  AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than

  proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.

  References:

  AAISM Study Guide - AI Governance and Program Management (Data Classification and Inventory)

  ISACA AI Security Management - Automated Data Cataloging for AI Projects

• Question 42:

  The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:

  A. ensure the development of powerful, efficient, and scalable AI systems
  B. deploy fast and cost-efficient AI systems for rapidly changing environments
  C. align the system components of AI with the business goals of the organization
  D. provide a basis for identification of threats and vulnerabilities
  C. align the system components of AI with the business goals of the organization

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  An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives. References:. AI Security Management. (AAISM) Body of Knowledge: AI Program Architecture -alignment of capabilities, processes, and controls to business objectives. AI Security Management. Study Guide: Architecture-driven governance, traceability from business goals to technical and control design

• Question 43:

  Which of the following BEST ensures AI components are validated as part of disaster recovery testing?

  A. Disconnecting primary model training clusters to test retraining workflow during extended outages
  B. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
  C. Running simulated data loss scenarios by erasing test records from the AI system's feature store
  D. Monitoring model performance metrics during failover and recovery to assess system stability
  D. Monitoring model performance metrics during failover and recovery to assess system stability

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  Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end. Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.

  References: AI Security Management. (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning-Metrics, Model Performance Validation, and Recovery Readiness."

• Question 44:

  An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:

  A. synthetic intrusion data to train the tool's components
  B. validation data sets to enable highly realistic AI decisions
  C. automated rule creation to increase model performance
  D. classified real intrusion data based on labeled data
  A. synthetic intrusion data to train the tool's components

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  AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing. Automated rules are a signature-based paradigm and

  do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.

  References:. AI Security Management. (AAISM) Body of Knowledge: Security data engineering; synthetic data via generative models for rare-event detection; adversarial augmentation for IDS.. AI Security Management. Study Guide: Model robustness with synthetic adversarial examples; training-set enrichment for anomaly detection.

• Question 45:

  An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?

  A. Performing model testing and validation
  B. Training the foundational model on large data sets
  C. Ensuring model developers have been trained in AI risk
  D. Fine-tuning the foundational model
  D. Fine-tuning the foundational model

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  AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a

  technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.

  References:

  AAISM Study Guide - AI Risk Management (Hallucination and Output Integrity Risks)

  ISACA AI Security Management - Fine-tuning Generative Models

• Question 46:

  A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?

  A. Maximizing neural network size
  B. Tuning algorithms used in the AI model
  C. Maximizing the amount of training data
  D. Selecting the appropriate training data
  D. Selecting the appropriate training data

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  AAISM prioritizes training data suitability-lawful sourcing, provenance, quality, representativeness, and safety-especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements. References:. AI Security Management. (AAISM) Body of Knowledge: Data Governance and Quality; High-Risk/Health Context Controls; Safety and Harm Minimization. AAISM Study Guide: Data Provenance and Suitability, Domain-Specific Dataset Controls; Compliance-by-Design for Sensitive Sectors

• Question 47:

  Employees are regularly using open-source generative AI without guidance. What should be the CISO's GREATEST concern?

  A. Model hallucinations
  B. Data leakage
  C. Lack of monitoring
  D. Policy violations
  B. Data leakage

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  AAISM emphasizes that the primary and most severe risk of uncontrolled generative AI usage is data leakage, including: confidential data regulated personal data intellectual property While hallucinations (A) are harmful, they do not cause direct data loss. Lack of monitoring (C) is a secondary risk. Policy violations (D) occur because of lack of controls but do not outweigh the impact of leaked sensitive data.

  References: AAISM Study Guide - Generative AI Risks; Human-Initiated Data Exposure.

• Question 48:

  Secure aggregation enhances the security of federated learning systems by:

  A. Processing client updates in isolation to reduce the risk of exposing sensitive information
  B. Applying differential privacy techniques to mask sensitive information in training data
  C. Encrypting individual model updates during transmission to ensure only the server can access the data
  D. Ensuring individual client contributions remain confidential even if the server is compromised
  D. Ensuring individual client contributions remain confidential even if the server is compromised

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  Secure aggregation cryptographically aggregates client updates so that the server learns only the sum /aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions-even if compromised-thereby preserving client confidentiality. Option C (encryption in transit) is insufficient because decryption at the server reveals updates; Option A is procedural, not cryptographic; Option B (differential privacy) is a separate technique and not the defining property of secure aggregation.

  References: AAISM Body of Knowledge: Privacy-Preserving ML-Federated Learning and Secure Aggregation; AAISM Study Guide: Threat Models for Aggregation Servers and Confidentiality Guarantees.

• Question 49:

  Which of the following is the BEST reason to immediately disable an AI system?

  A. Excessive model drift
  B. Slow model performance
  C. Overly detailed model outputs
  D. Insufficient model training
  A. Excessive model drift

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  According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in

  production is excessive drift compromising reliability.

  References:

  AAISM Exam Content Outline - AI Governance and Program Management (Model Drift Management)

  AI Security Management Study Guide - Disabling AI Systems

• Question 50:

  A CISO must provide KPIs for the organization's newly deployed AI chatbot. Which metrics are BEST?

  A. Response time and throughput
  B. Error rate and bias detection
  C. Customer effort score and user retention
  D. Explainability and F1 score
  B. Error rate and bias detection

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  AAISM recommends that AI KPIs should emphasize:

  Error rates - measure correctness and reliability

  Bias detection metrics - assess fairness and harm risk These are the core governance KPIs for AI systems interacting with end users. Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business-focused. F1 score (D) is useful but not as critical as bias monitoring.

  References: AAISM Study Guide - AI Performance, Fairness and Governance Metrics.