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ISACA AAIA Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Operations- AI Data Management
  • 1. Operational AI controls
  • 2. Training and testing data
  • 3. Data quality and integrity
- AI System Lifecycle
  • 1. Configuration and change management
  • 2. AI system development and deployment
  • 3. Monitoring and maintenance
Topic 2: AI Governance and Risk- AI Security and Controls
  • 1. Vendor and supply chain risk
  • 2. Identity and access management
  • 3. Threat and vulnerability management
- AI Governance Frameworks
  • 1. AI policies and governance structures
  • 2. AI risk management
  • 3. Regulatory and compliance considerations
Topic 3: AI Auditing Tools and Techniques- Audit Testing and Reporting
  • 1. Audit reporting and follow-up
  • 2. Evidence collection techniques
  • 3. Testing and sampling methodologies
- AI Audit Planning
  • 1. Audit scoping and objectives
  • 2. AI audit methodologies
  • 3. Audit resource planning

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ISACA Advanced in AI Audit Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

Answer: B

Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."


NEW QUESTION # 52
An organization using AI to create digital content faces challenges in protecting its intellectual property.
Which of the following is the BEST way to mitigate this risk?

Answer: B

Explanation:
For AI-generated content (images, text, or audio), " watermarking " is a critical technical control to protect Intellectual Property (IP) and ensure provenance. Digital watermarks can be embedded into the metadata or the content itself (often invisibly) to prove the organization is the creator and to track unauthorized distribution. This supports legal claims and brand protection. Firewalls and data separation protect the system , but watermarking protects the output once it is shared online or with clients. As AI makes content creation easier, the ability to prove ownership becomes an essential ethical and legal safeguard.


NEW QUESTION # 53
Which of the following is the PRIMARY advantage of using K-fold cross validation when evaluating the performance of a machine learning (ML) model?

Answer: D

Explanation:
The primary advantage of K-fold cross validation is that it uses multiple train/test splits, cycling through all folds so that each observation is used both for training and testing at different points.
This process provides a more reliable estimate of model performance and reduces the risk of overfitting to a single split (option D). It is an established best practice in model evaluation and aligns with AAIA's emphasis on testing techniques for AI solutions and data analytics.


NEW QUESTION # 54
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?

Answer: B

Explanation:
Representativeness means that training data accurately reflects the current real-world environment in which the AI system operates. The BEST way to ensure this is by verifying that the training data remains relevant and aligned with evolving real-world conditions (C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.


NEW QUESTION # 55
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

Answer: A

Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIA™ Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies." While hallucinations (C) and privacy notices (B) are important concerns, the primary auditor responsibility is to ensure that source data is accurate and suitable. Therefore, D is the correct response.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Auditor Responsibility and AI Input Validation"


NEW QUESTION # 56
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