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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Real Exam Qty:	90
Passing Score:	65%
Related Certifications:	CIA CPA CISA FCCA ACCA
Exam Format:	Multiple Choice, Remotely Proctored, Computer-Based
Available Languages:	English
Exam Duration:	120 minutes
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Online remotely proctored computer-based exam
Pre Condition:	Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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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	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 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.

ISACA Advanced in AI Audit Sample Questions (Q57-Q62):

NEW QUESTION # 57
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?

A. Model inversion
B. Model poisoning
C. Model drift
D. Model extraction

Answer: C

Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIA™ manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations. Options A, B, and C refer to security attacks, whereas " Model drift " is the natural operational degradation that occurs when AI systems are not regularly updated with fresh, representative data.

NEW QUESTION # 58
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 # 59
A company uses generative AI to draft customer-facing emails. Which risk is MOST specific to generative AI (versus traditional predictive models)?

A. Feature leakage
B. Class imbalance
C. Hallucination - generating plausible but factually incorrect content
D. Overfitting to training data

Answer: C

Explanation:
Hallucination is a risk largely unique to generative AI, where the model produces fluent, confident- sounding output that is factually inaccurate or fabricated.

NEW QUESTION # 60
Which of the following is the BEST recommendation for an organization that has adopted "vibe coding" (using AI to generate code based on high-level natural language prompts)?

A. Discontinue the current practice.
B. Build the organization's own cryptography.
C. Prompt the AI to review its own code.
D. Adopt a security checklist for code review.

Answer: D

Explanation:
"Vibe coding" or AI-assisted development carries the risk of introducing "hallucinated" vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led "security checklist" for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials.

NEW QUESTION # 61
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?

A. Creating dummy variables for the data attribute dog breed for the options labrador, terrier, beagle
B. Creating dummy variables for the data attribute product flavor for the options vanilla, chocolate, strawberry, banana
C. One-hot encoding the data attribute car colors for the options red, blue, green, black, white
D. One-hot encoding the data attribute customer rewards category for the options economy, business, first class

Answer: D

Explanation:
The AAIATM Study Guide emphasizes that encoding categorical variables must preserve the semantic meaning and order of categories when relevant. The greatest risk occurs when ordinal data--such as customer rewards tiers--is treated as nominal through one-hot encoding, which removes the inherent order and may impair model learning.
"Improper encoding of ordinal variables as nominal can distort the model's understanding of relationships, leading to inaccurate predictions or biased outcomes." Customer reward categories (economy < business < first class) have a natural order. One-hot encoding ignores this order, potentially degrading model accuracy. Other options represent nominal data and are appropriately encoded.

NEW QUESTION # 62
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