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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Operations	- AI Data Management 1. Training and testing data 2. Data quality and integrity 3. Operational AI controls - AI System Lifecycle 1. AI system development and deployment 2. Configuration and change management 3. Monitoring and maintenance
Topic 2: AI Auditing Tools and Techniques	- Audit Testing and Reporting 1. Evidence collection techniques 2. Audit reporting and follow-up 3. Testing and sampling methodologies - AI Audit Planning 1. Audit resource planning 2. AI audit methodologies 3. Audit scoping and objectives
Topic 3: 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. AI risk management 2. Regulatory and compliance considerations 3. AI policies and governance structures

Section

Objectives

Topic 1: AI Operations

- AI Data Management

1. Training and testing data
2. Data quality and integrity
3. Operational AI controls

- AI System Lifecycle

1. AI system development and deployment
2. Configuration and change management
3. Monitoring and maintenance

Topic 2: AI Auditing Tools and Techniques

- Audit Testing and Reporting

1. Evidence collection techniques
2. Audit reporting and follow-up
3. Testing and sampling methodologies

- AI Audit Planning

1. Audit resource planning
2. AI audit methodologies
3. Audit scoping and objectives

Topic 3: 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. AI risk management
2. Regulatory and compliance considerations
3. AI policies and governance structures

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ISACA Advanced in AI Audit Sample Questions (Q295-Q300):

NEW QUESTION # 295
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?

A. Incident response plan
B. Benchmarking against peer organizations
C. Business disruption and financial impact
D. Intellectual property leakage and invalidation

Answer: C

Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error--such as false customer rejection or failure to detect fraudulent accounts. The AAIATM Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines. KYC models must be assessed for how errors can lead to compliance failures or reputational harm."

NEW QUESTION # 296
Which of the following AI solutions is BEST used to generate standardized reports using unstructured customer feedback?

A. Anomaly detection
B. Predictive analytics
C. Natural language processing (NLP)
D. Recommendation engines

Answer: C

Explanation:
Natural Language Processing (NLP) is the branch of AI specifically designed to handle unstructured text. In an audit or business context, NLP tools can perform "sentiment analysis" and "entity extraction" on large volumes of customer feedback to identify common themes, complaints, or positive trends. According to ISACA, NLP is the optimal choice for converting qualitative, human-written text into structured, standardized reports that auditors can use for objective analysis.

NEW QUESTION # 297
An organization uses an AI-powered tool to detect and respond to cybersecurity threats in real time. An IS auditor finds that the tool produces excessive false positives, increasing the workload of the security team. Which of the following techniques should the auditor recommend to BEST evaluate the tool's effectiveness in managing this issue?

A. Use a log analysis tool to examine the types and frequency of alerts generated.
B. Implement a benchmarking tool to compare the system's alerting capability with industry standards.
C. Deploy a machine learning (ML) validation tool to increase the model's accuracy and performance.
D. Conduct penetration testing to assess the system's ability to detect genuine threats.

Answer: C

NEW QUESTION # 298
An IS auditor is evaluating an organization's incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?

A. The program prioritizes incidents based on alignment with industry leading practices.
B. The program uses past AI-related incidents and resolutions to categorize current incidents.
C. The program includes processes to respond to AI model drift and data integrity attacks.
D. The program mandates retraining AI systems after incidents are investigated.

Answer: C

Explanation:
AI-related incidents often differ significantly from traditional IT incidents due to their dependence on data, model behavior, and algorithm performance. According to the AAIATM Study Guide, incident management programs must include capabilities specifically tailored to AI, such as detecting and mitigating model drift and safeguarding against data poisoning or integrity attacks.
"AI incident response frameworks must account for issues unique to machine learning, including model drift, adversarial inputs, and data integrity breaches. An effective program incorporates detection, response, and recovery mechanisms for these AI-specific threats."

NEW QUESTION # 299
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?

A. One-hot encoding was utilized.
B. Ordinal label encoding was utilized.
C. Clustering was not utilized.
D. Data scaling was not utilized.

Answer: B

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 # 300
......

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