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ISACA AAIA Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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.

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ISACA Advanced in AI Audit Sample Questions (Q119-Q124):

NEW QUESTION # 119
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

A. To identify and mitigate potential data drift within models
B. To improve the accuracy and performance of the systems
C. To align AI system development with organizational values and principles
D. To ensure the systems align with the preservation of individual rights

Answer: D

Explanation:
The AAIA™ Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.
"Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process.
This is foundational in building public trust and avoiding legal liabilities." Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Ongoing Ethical Oversight"

NEW QUESTION # 120
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?

A. Unlabeled data sets
B. Clustered data sets
C. Labeled data sets
D. Randomized data sets

Answer: C

NEW QUESTION # 121
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?

A. Use a platform that certifies the provenance and licensing of its training data.
B. Implement a manual review process to ensure no copyrighted images are used in generated outputs.
C. Label all AI-generated images to disclaim the possibility of third-party content.
D. Suspend the use of the platform until the training data is sanitized.

Answer: A

Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"

NEW QUESTION # 122
An IS auditor reviewed an AI-enabled software for processing a bank's financial information and discovered errors in the training data. Which of the following would BEST mitigate this risk?

A. Model validation on benchmark data
B. User interface testing
C. Data quality testing
D. Functional testing of the application

Answer: C

Explanation:
When errors are discovered in training data, the primary risk is that themodel has learned from incorrect or low-quality inputs, which can systematically distort outputs (e.g., misstatements of financial information, incorrect classifications, or erroneous flags).Data quality testingdirectly targets this issue by assessing completeness, accuracy, consistency, validity, and integrity of the data used to train, validate, and test AI models.
Functional testing (A) focuses on whether the application behaves according to requirements, not specifically on the underlying data quality. User interface testing (C) concerns usability and presentation, not training data. Model validation on benchmark data (D) helps assess performance but may not reveal or correct root# cause issues in the underlying training data if those benchmarks are also flawed or unrepresentative. AAIA content emphasizes thatdata quality controls are foundationalto AI reliability and must be treated as a core operational control.
References:
ISACA,Advanced in AI Audit (AAIA) Exam Content Outline- Domain 2: Data Management Specific to AI (data quality, data balancing, data security).
ISACA AI guidance on data quality controls as the basis of trustworthy AI outcomes.

NEW QUESTION # 123
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:

A. significantly reduce data bias.
B. ensure full compliance with regulations.
C. identify complex data patterns.
D. eliminate the need for human intervention.

Answer: C

Explanation:
AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIA™ Study Guide states, "AI-based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually." AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Advantages of AI-Enabled Audit Approaches"

NEW QUESTION # 124
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Carl Ford Carl Ford

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