Get Valid ISACA AAIA Exam Questions and Answer

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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 3
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

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ISACA Advanced in AI Audit Sample Questions (Q149-Q154):

NEW QUESTION # 149
Which of the following is the BEST way to mitigate data poisoning in an AI model?

Answer: D

Explanation:
Data poisoningoccurs when attackers manipulate training data to corrupt model behavior. The most direct mitigation is to implementrobust data validation and integrity checks(option C), including anomaly detection on input distributions, provenance controls, verification of data sources, and safeguards for pipelines feeding the training set. AAIA highlightsthreats and vulnerabilities specific to AIand the importance of controls that protect data integrity in AI Operations.
Option A (relying on third-party providers) does not inherently eliminate poisoning risk; providers themselves may be vulnerable. Option B (increasing data size) can dilute but not reliably remove malicious samples.
Option D (simpler algorithms) might help interpretability but does not directly prevent poisoned data from influencing the model. The most effective, aligned control isrigorous data validationto ensure only trustworthy data enters the training process.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI (controls for AI- related threats).
ISACA risk guidance referencing data integrity and poisoning risks in AI pipelines.


NEW QUESTION # 150
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

Answer: D

Explanation:
Adversarial attacks involve " manipulated inputs " (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must " Validate the process for handling manipulated inputs. " This includes checking for robust input validation, anomaly detection on incoming data, and " adversarial training " where the model is intentionally exposed to these attacks during development to build resilience. Focusing on logs or speed (Options A, B, and C) does not address the fundamental vulnerability of the algorithm to technical manipulation.


NEW QUESTION # 151
When an IS auditor reviews audit evidence gathered by AI, which of the following practices should the auditor perform to ensure the reliability of the evidence?

Answer: D

Explanation:
Professional skepticism is a core requirement for auditors using AI tools. Because AI can suffer from " hallucinations " or misinterpretation of data, the auditor must " Validate the evidence against source documents. " This involves taking a sample of the AI ' s findings and manually verifying them against the original records (e.g., invoices, contracts, or logs). According to the ISACA AAIA™ Study Guide, while reviewing model cards (Option A) provides context on the tool ' s capabilities, it does not confirm that a specific piece of evidence is accurate. Direct verification against the " Ground Truth " (source documents) is the only way to ensure the reliability and integrity of AI-generated audit evidence.


NEW QUESTION # 152
Which of the following BEST helps an organization manage bias in AI model decisions?

Answer: D

Explanation:
Bias is often subtle and context-dependent, making it difficult for automated systems to detect on their own. " Human oversight and feedback mechanisms " (Human-in-the-loop) allow domain experts to review model decisions and flag outcomes that appear discriminatory or unfair. According to the AAIA™ framework, these human feedback loops are critical for " correcting " the model ' s logic over time. While standardizing criteria (Option A) is a good starting point, humans provide the necessary " common sense " and ethical judgment that machines lack. Retraining (Option D) without human-validated data may simply reinforce the existing biases rather than eliminate them.


NEW QUESTION # 153
An IS auditor has discovered that an organization ' s AI system is less accurate than the required threshold and recommends management implement cross-referencing multiple models in order to produce more accurate results. This is an example of which type of risk response?

Answer: A

Explanation:
Risk mitigation involves implementing controls or actions to reduce the likelihood or impact of a risk to an acceptable level. In this scenario, the model ' s failure to meet accuracy thresholds poses a performance and operational risk. By recommending the use of model ensembles or cross-referencing (where multiple models evaluate the same input), the auditor is proposing a technical control designed to improve the reliability of the system ' s output. This reduces the risk of incorrect decisions without eliminating the system entirely (Avoidance) or moving the risk to a third party (Transfer). Mitigation is the standard response for improving AI performance while maintaining the existing use case.


NEW QUESTION # 154
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