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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.

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ISACA Advanced in AI Audit Sample Questions (Q210-Q215):

NEW QUESTION # 210
When auditing AI systems, which data management practice BEST ensures compliance with privacy regulations?

A. Implementing data minimization principles during data collection
B. Retaining raw data indefinitely for future use
C. Using multiple data sources across different regions
D. Increasing dataset size to improve model accuracy

Answer: A

Explanation:
Privacy regulations like GDPR and CCPA emphasize the principle of " Data Minimization " -collecting only the data that is strictly necessary for the intended purpose. In an AI context, organizations often feel tempted to collect as much data as possible to improve accuracy (Option A), but this increases privacy risk and legal exposure. According to ISACA, implementing data minimization is the most effective practice for ensuring regulatory compliance. It reduces the " attack surface " in the event of a breach and ensures that the organization is not holding onto sensitive PII that it cannot justify. Indefinite retention (Option D) is a direct violation of most modern privacy laws.

NEW QUESTION # 211
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?

A. Analysis of variance
B. Logistic regression
C. Log-rank test
D. Image recognition

Answer: B

Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral). Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.

NEW QUESTION # 212
An IS auditor is reviewing a dataset used by a university. Which of the following MOST likely indicates a risk that the model could not process all data and make necessary correlations?

A. Student Number field in integer format
B. Final Grade Percent field in object format
C. Having an undergraduate degree in Boolean format
D. Grade Level field in float format

Answer: B

Explanation:
In machine learning, "Data Typing" is foundational. If a numerical field (like Grade Percent) is stored as an "Object" (string/text), the AI model cannot perform mathematical operations on it, such as calculating correlations or averages. According to the AAIATM manual, this is a "Data Quality" failure that leads to "Feature Exclusion," where the model simply ignores the variable or treats each number as a unique text label, losing all quantitative meaning. The other formats (Integer, Float, Boolean) are appropriate for their respective data types.

NEW QUESTION # 213
During a walkthrough, an auditor learns that model retraining occurs automatically without any approval gate. What is the GREATEST risk this presents?

A. Reduced explainability
B. Increased computing costs
C. Slower model training time
D. Unvalidated model changes being deployed to production, potentially degrading performance or introducing bias

Answer: D

Explanation:
Automated retraining without a human approval/validation gate can silently push a degraded or biased model into production, bypassing change management controls.

NEW QUESTION # 214
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?

A. Open-source data augmentation files
B. Social media platform with images from all over the world
C. Cohort of dermatologists with signed patient consent forms
D. Research facility receiving grants for cancer research

Answer: B

Explanation:
Images fromsocial media platforms(C) pose the greatest risk todata integritybecause:
* Images may be heavily filtered or edited
* Lighting, scale, and resolution are inconsistent
* Metadata is unreliable
* Medical diagnosis labels (if any) are unverified
* Context is unknown and may include misinformation
* Sources cannot be validated or authenticated
AAIA stresses that medical AI systems requirehigh-quality, clinically validated datasetswith clear provenance, accuracy, and consent.
Research facilities (A) and dermatologist cohorts (D) provide medically reliable, high-integrity data.
Open-source augmentation files (B) are synthetic and secondary, but still safer than uncontrolled, unlabeled social media images.
References:
AAIA Domain 2: Data Quality, Provenance, Accuracy, and Label Integrity
AAIA Domain 1: Risk Identification in High-Impact AI Systems

NEW QUESTION # 215
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