Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 241:

  Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

  A. Lineage
  B. Transformation
  C. Origin
  D. Processing
  A. Lineage

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  Data lineage records and monitors the end-to-end journey of data-sources, movements, transformations, storage locations, uses, and dependencies-providing traceability, auditability, and accountability across the AI lifecycle. "Origin" is a single point (provenance), "transformation" is one step within the flow, and "processing" is a general activity rather than a governance record of the entire path.

  References: AI Security Management. (AAISM) Body of Knowledge: Data Governance-Provenance and Lineage; AAISM Study Guide: Lineage Documentation, Traceability, and Audit Evidence.

• Question 242:

  Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?

  A. Inaccurate generalizations from new data by the AI model
  B. Weak controls for access to the AI model
  C. Lack of protection against denial of service (DoS) attacks
  D. Inability to detect input modifications causing inappropriate AI outputs
  B. Weak controls for access to the AI model

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  AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for

  attackers. As such, it is identified as the most easily exploited vulnerability.

  References:

  AAISM Exam Content Outline - AI Risk Management (Access and Authentication Vulnerabilities)

  AI Security Management Study Guide - Exploitable Weaknesses in AI Systems

• Question 243:

  Which AI model is BEST suited to ensure explainability in an HR department's pre-screening tool for candidate resumes?

  A. Support vector machine
  B. Neural network
  C. Decision tree
  D. Gradient boosting machine
  C. Decision tree

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  According to AAISM, decision trees provide the highest explainability because their structure clearly shows how inputs map to decisions. This is essential in HR applications subject to fairness, bias, and compliance requirements. SVMs (A) and gradient boosting (D) are less interpretable. Neural networks (B) are explicitly listed as low-explainability models.

  References: AAISM Study Guide - Explainability and Transparency Requirements; Interpretable ML Models.

• Question 244:

  What BEST ensures a proper business continuity plan (BCP) for an AI solution?

  A. Enhancing monitoring for model failure
  B. Testing AI infrastructure failover mechanisms
  C. Implementing access controls
  D. Increasing backup restoration detail
  B. Testing AI infrastructure failover mechanisms

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  AAISM defines an AI BCP as requiring validated failover and recovery testing of AI components, including: . model hosting environments . model-serving APIs . feature stores . inference pipelines Only Option B tests actual continuity of AI functionality. Monitoring (A) detects issues but does not ensure continuity. Access controls (C) relate to security, not

  continuity. Backup detail (D) is insufficient without recovery testing.

  References: AAISM Study Guide - AI Resilience and Disaster Recovery Validation.

• Question 245:

  Which BEST describes the role of model cards in AI solutions?

  A. They visualize AI model performance
  B. They document training data and AI model use cases
  C. They help developers create synthetic data
  D. They automatically fine-tune AI models
  B. They document training data and AI model use cases

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  AAISM explains that model cards provide structured documentation about AI models, including: intended use cases training data characteristics ethical considerations known limitations risk factors performance benchmarks They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).

  References: AAISM Study Guide - AI Transparency and Model Cards.

• Question 246:

  Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

  A. F1 score
  B. Recall
  C. Precision
  D. Specificity
  B. Recall

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  AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official

  study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.

  References:

  AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance)

  ISACA AI Security Management - Model Accuracy and Completeness Assessments

• Question 247:

  Which of the following is the MOST likely cause of model drift?

  A. Data poisoning B. Perfect knowledge
  B. Membership inference
  C. Model stealing
  A. Data poisoning B. Perfect knowledge

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  Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data-centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-

  generating process the model learns from. In contrast:

  Perfect knowledge is an attacker capability descriptor, not a drift cause.

  Membership inference targets privacy of the training set and does not inherently shift data distributions.

  Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.

  References:. AI Security Management (AAISM) Body of Knowledge: Model Risk and Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion. AAISM Study Guide: Production Monitoring and Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls. AAISM Mapping to Standards: Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback

• Question 248:

  Which of the following is the GREATEST risk inherent to implementing generative AI?

  A. Lack of employee training
  B. Unidentified asset vulnerabilities
  C. Inadequate return on investment (ROI)
  D. Potential intellectual property violations
  D. Potential intellectual property violations

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  The AAISM framework identifies intellectual property (IP) violations as the most significant inherent risk in deploying generative AI. These systems often rely on large-scale internet data for training, which may inadvertently contain copyrighted or proprietary material. This creates legal and reputational exposure when outputs reproduce or reference protected content. While employee training gaps, asset vulnerabilities, and ROI concerns are relevant risks, they are not inherent to generative models

  themselves. The greatest inherent risk tied directly to generative AI adoption is the possibility of violating intellectual property rights.

  References:

  AAISM Study Guide - AI Risk Management (Generative AI Risks and Legal Exposure)

  ISACA AI Security Management - Copyright and IP Concerns in Generative AI

• Question 249:

  When preparing for an AI incident, which of the following should be done FIRST?

  A. Establish recovery processes for AI system models and datasets
  B. Establish a cross-functional incident response team with AI knowledge
  C. Implement a clear communication channel to report AI incidents
  D. Create containment and eradication procedures for AI-related incidents
  B. Establish a cross-functional incident response team with AI knowledge

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  AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.

  References:

  AI Security Management. (AAISM) Body of Knowledge: Incident Management-Preparation; Roles and Responsibilities; Cross-functional Coordination.

  AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority and Escalation. AAISM Mapping to Standards: Security Operations-Preparation Before Procedures (people and roles precede playbooks)

• Question 250:

  A data scientist creating categories and training an algorithm on large data sets is performing which learning technique?

  A. Supervised
  B. Reinforcement
  C. Unsupervised
  D. Machine learning (ML)
  A. Supervised

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  AAISM identifies supervised learning as involving:

  labeled categories

  ground-truth datasets

  model training with known outcomes

  This aligns exactly with categorizing data and training on labeled datasets.

  Reinforcement (B) involves reward feedback loops. Unsupervised (C) uses unlabeled data. "Machine learning" (D) is too broad and not a specific technique.

  References: AAISM Study Guide - AI Learning Types; Supervised Learning Definition.