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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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.

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ISACA Advanced in AI Audit Sample Questions (Q190-Q195):

NEW QUESTION # 190
An IS auditor is evaluating an organization's data governance controls for its AI system. Which of the following represents the GREATEST risk in this context?

A. Inadequate controls over data accuracy and privacy compliance
B. Lack of procedures for automated data backup
C. Limited frequency of AI system performance and data accuracy reviews
D. Inconsistent data management practices

Answer: A

Explanation:
In AI systems,data accuracy and privacyare foundational to both performance and regulatory compliance.
Inadequate controls over data accuracy and privacy compliance(D) pose thegreatest risk, as they can lead to incorrect decisions, legal violations, regulatory penalties, and significant reputational damage. AAIA emphasizes that data governance programs must ensure data is accurate, secure, lawfully processed, and appropriately protected throughout the AI life cycle.
Option A (inconsistent practices) is concerning but is often a symptom of underlying governance weaknesses; its impact is most severe when it affects accuracy and privacy. Option B focuses on backup procedures, which are important for availability and resilience, but not as central to AI decision quality and legal risk. Option C (limited performance and accuracy reviews) is serious but again narrower than outright inadequacy of key controls over accuracy and privacy.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs (accuracy, privacy, and governance controls).
ISACA data governance and privacy guidance for AI systems.

NEW QUESTION # 191
An IS auditor uses an internally developed generative AI tool to prepare a status update for audit stakeholders. Which of the following is the auditor's MOST appropriate course of action?

A. Compare results with a publicly available generative AI tool to ensure outputs are similar.
B. Share and review the results with management.
C. Regenerate the results to ensure similar outputs are provided.
D. Assess whether the information provided is complete and accurate.

Answer: D

Explanation:
Auditors using AI tools to generate reports or updates must ensure the output is reliable, factually accurate, and complete. As per the AAIATM Study Guide, accountability for audit content remains with the auditor, even when generative AI assists with content creation. Therefore, verifying the completeness and correctness of AI-generated content is the primary responsibility.
"AI-generated audit outputs must be validated against source data and professional standards.
The auditor must critically assess AI contributions and not rely on them without human oversight."

NEW QUESTION # 192
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

A. Training models on historical audit results generated prior to AI adoption
B. Applying automated anomaly detection without human oversight
C. Utilizing unstructured data sources without standardized preprocessing
D. Embedding AI audit tools in transactional systems without user training

Answer: C

Explanation:
Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.
AAIA warns that improperly processed unstructured data leads to:
* Incorrect text extraction
* Lost contextual meaning
* Feature extraction errors
* Misclassification
* Inaccurate audit evidence
Option B is a bias or relevance risk, not data quality.
Option C is a governance/training issue.
Option D is an oversight risk, not a data quality issue.
Therefore, using unstructured data without preprocessing is the most direct threat to data quality.
References:
AAIA Domain 2: Data Preprocessing and Quality
AAIA Domain 3: AI-Assisted Audit Evidence Integrity

NEW QUESTION # 193
Which of the following is the MOST frequent cause of generative AI model hallucinations?

A. Weak access controls
B. Inadequate data quality
C. Outdated model cards
D. Inconsistent change management

Answer: B

Explanation:
Hallucinations--instances where a generative model produces factual errors or nonsensical information with high confidence--are primarily caused by "Inadequate data quality" in the training set. If the model is trained on data that is contradictory, incomplete, or contains "noise" (incorrect facts), it fails to learn accurate semantic relationships. The ISACA AAIATM manual highlights that
"Data Cleaning" and "Provenance" are essential to mitigate this.

NEW QUESTION # 194
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model ' s correlations?

A. Converting categorical variables to numerical formatting and encodings
B. Changing the total transaction amount data type from object to integer
C. Deleting duplicate entries because all attributes in the record match
D. Deleting outlier data values outside of the 5th and 95th percentiles from source data

Answer: D

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 # 195
......

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