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ISACA AAIA Exam Syllabus Topics:

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

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ISACA Advanced in AI Audit Sample Questions (Q89-Q94):

NEW QUESTION # 89
Which of the following techniques BEST assesses an AI model's ability to generalize to new data?

Answer: A

Explanation:
K-fold cross validationpartitions the dataset intokequally sized folds, iteratively using one fold for testing and the remaining folds for training. By rotating through all folds, the model is evaluated on multiple, non- overlapping subsets of data, providing a robust estimate of generalization performance. This process reduces the risk of overfitting to a single train-test split and is explicitly recognized in AI testing techniques and data analytics practices relevant to AAIA.
Option A (failure mode effects analysis) is useful for understanding failure impacts but not primarily for generalization. Option B (unit testing) focuses on code components, not model generalization. Option D (metamorphic testing) is valuable for validating behavior under transformations, but the standard, widely accepted approach to assessing generalization remainscross validation, making option C the best answer.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Testing Techniques for AI Solutions (conventional and AI- specific testing).
ISACA AI and data analytics training content that discusses cross validation as a generalization assessment method.


NEW QUESTION # 90
An IS auditor for a veterinary clinic was informed that the dog breed categorical variable is necessary for the predictive model. Which of the following introduces the MOST risk?

Answer: D

Explanation:
Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3).
This falsely implies hierarchy or ranking among breeds, introducing bias into the model.
AAIA emphasizes that incorrect encoding of categorical variables can cause:
Artificially weighted importance
Misleading correlations
Incorrect predictions based on false order


NEW QUESTION # 91
Which of the following is the PRIMARY benefit of using linear regression in machine learning (ML) when transparency and auditability are required?

Answer: B

Explanation:
Linear regression is a "transparent" or "white-box" model. Its primary benefit in an audit context is its interpretability; each coefficient directly indicates the influence of an input variable on the output. This allows auditors to easily verify the logic and ensure that no inappropriate or discriminatory variables are driving the results. While linear models are less prone to complex overfitting than deep learning, they are not immune to it. Their simplicity often results in lower predictive accuracy for complex data, but for auditability and compliance, the ability to explain the
"why" behind a prediction is paramount.


NEW QUESTION # 92
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model ' s correlations?

Answer: A

Explanation:
While removing outliers can sometimes help with model performance, " Deleting outlier data values " arbitrarily poses the greatest risk to reliability because it can remove rare but legitimate data points that are critical for accurate pattern recognition. In domains like fraud detection or risk management, the outliers are the most important data. Distorting the natural distribution of data can lead to skewed correlations and a model that fails to recognize high-impact " tail " events. Encoding (Option B) and removing true duplicates (Option D) are standard, low-risk preprocessing steps. Correcting data types (Option C) is essential for enabling mathematical operations and is not a risk.


NEW QUESTION # 93
An organization plans to implement an AI search and chatbot solution provided by an external vendor. Which of the following is MOST important for an IS auditor to confirm?

Answer: B

Explanation:
For third-party (SaaS) AI solutions, the organization relies on the vendor's internal controls to ensure data security, privacy, and model integrity. The AAIA™ manual highlights that the most critical confirmation is " Whether the vendor can provide an independent third-party attestation " (such as a SOC 2 Type II or ISO/IEC
42001 report). This provides the organization with reliable evidence that the vendor's AI governance and security practices have been verified by an external auditor. While metrics like BLEU (Option A) or technical integration (Option C) are important for performance, they do not provide the necessary governance assurance required for organizational risk management.


NEW QUESTION # 94
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