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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
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 resource planning 2. Audit scoping and objectives 3. AI audit methodologies
AI Operations	- AI System Lifecycle 1. AI system development and deployment 2. Configuration and change management 3. Monitoring and maintenance - AI Data Management 1. Training and testing data 2. Data quality and integrity 3. Operational AI controls
AI Governance and Risk	- AI Governance Frameworks 1. AI risk management 2. AI policies and governance structures 3. Regulatory and compliance considerations - AI Security and Controls 1. Identity and access management 2. Threat and vulnerability management 3. Vendor and supply chain risk

Section

Objectives

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

AI Operations

- AI System Lifecycle

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

- AI Data Management

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

AI Governance and Risk

- AI Governance Frameworks

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

- AI Security and Controls

1. Identity and access management
2. Threat and vulnerability management
3. Vendor and supply chain risk

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ISACA Advanced in AI Audit Sample Questions (Q173-Q178):

NEW QUESTION # 173
A company deploys a third-party AI vendor's model without visibility into its training data or architecture. What is the PRIMARY audit concern?

A. Lack of transparency creating an inability to assess bias, drift, or explainability
B. Higher licensing costs
C. Slower inference speed
D. Vendor lock-in

Answer: A

NEW QUESTION # 174
Which of the following is the MOST important task when gathering data during the AI system development process?

A. Training the system
B. Isolating the system
C. Stratifying the data
D. Cleaning the data

Answer: D

Explanation:
Data cleaning is a foundational task in the AI development lifecycle. The AAIA™ Study Guide identifies data quality-ensuring completeness, accuracy, consistency, and correctness-as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.
"Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks." While training (D) is essential, it must occur only after the data has been adequately prepared. Stratification (A) supports certain modeling approaches but is secondary to data integrity. Therefore, C is the most important task at the data-gathering stage.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Collection and Preparation"

NEW QUESTION # 175
The PRIMARY benefit of using AI in audit fieldwork is that it:

A. Automates documentation of workpapers and artifacts.
B. Saves time for the engagement manager in reviewing the audit work.
C. Helps auditors to cover testing processes of all risk levels.
D. Can scan large datasets to identify trends and anomalies.

Answer: D

Explanation:
According to the AAIATM manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk- based approach.

NEW QUESTION # 176
An IS auditor uses an AI model to summarize worksheets, but several worksheets contained a " Hidden Cell " instructing the model to ignore control failures. Which solution BEST mitigates the risk?

A. Ensure read-only mode with track changes is enabled.
B. Convert files to PDF before uploading to the AI model.
C. Allow extraction only to predefined values and headers in the worksheets.
D. Instruct the model to ignore instructions in data and set high temperature.

Answer: C

Explanation:
This is a " Data-based Prompt Injection, " where the AI treats data as instructions. By " Allowing extraction only to predefined values and headers, " the auditor restricts the AI ' s " Context Window " to specific, known data fields. This prevents the AI from " reading " and executing malicious commands hidden in other cells.
High temperature (Option A) would only make the model more likely to follow erratic instructions.
Converting to PDF (Option D) is ineffective because modern AI models can still read the hidden text layers within a PDF.

NEW QUESTION # 177
Which of the following is the BEST way to test for proxy discrimination in a model that does not directly use a protected attribute (e.g., race)?

A. Test whether other correlated features (e.g., zip code) act as proxies that reproduce the same disparate outcomes
B. Reduce the number of training epochs
C. Increase the model's regularization parameter
D. Remove all numeric features

Answer: A

Explanation:
Proxy discrimination occurs when non-protected features correlate strongly with a protected attribute, effectively reintroducing bias. Correlation and outcome analysis on such features helps detect this.

NEW QUESTION # 178
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