Get Latest ISACA AAIA Exam Dumps [2026]

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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	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	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 (Q22-Q27):

NEW QUESTION # 22
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?

A. Increased computational cost due to redundant input checks
B. Overfitting caused by excessive anomaly filtering
C. Biased inputs that can affect credit decisions
D. Delayed model training cycles from excessive data preprocessing

Answer: C

Explanation:
In neural network-based credit scoring, the model ' s complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak, " biased inputs " or " noise " from various sources can seep into the training and inference pipelines. According to the ISACA AAIA™ framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions. While overfitting (Option B) and costs (Option C) are technical concerns, the systemic risk of automated discrimination via unchecked inputs poses the most significant threat to the organization ' s legal standing and reputation.

NEW QUESTION # 23
Which of the following is the BEST indicator that an organization has mature MLOps practices?

A. Models are deployed directly by data scientists without a pipeline
B. All models are retrained manually once per year
C. Automated CI/CD pipelines with testing, validation, monitoring, and rollback capabilities
D. Model documentation is optional

Answer: C

Explanation:
Mature MLOps integrates automated testing, validation gates, continuous monitoring, and rollback mechanisms into the deployment pipeline, mirroring DevOps discipline applied to machine learning.

NEW QUESTION # 24
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. One-hot encoding the data attribute customer rewards category for the options economy, business, first class
C. Creating dummy variables for the data attribute product flavor for the options vanilla, chocolate,strawberry, banana
D. One-hot encoding the data attribute car colors for the options red, blue, green, black, white

Answer: B

Explanation:
The AAIA™ 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.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Preprocessing and Feature Engineering"

NEW QUESTION # 25
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?

A. Intellectual property leakage and invalidation
B. Benchmarking against peer organizations
C. Business disruption and financial impact
D. Incident response plan

Answer: C

NEW QUESTION # 26
An IS auditor reviews an AI tool using K-means to cluster customers. One cluster shows very high spending but low product diversity. What should the auditor recommend?

A. Replace K-means clustering with a supervised learning model for more accurate analysis.
B. Document the algorithm failed because high spending customers did not exhibit high product diversity.
C. Increase the number of clusters to better capture variations in spending behavior.
D. Treat the cluster as a potentially valid segment of loyal customers with limited product interest.

Answer: D

Explanation:
K-means clustering is anunsupervised learningtechnique that groups data based on similarity. Discovering a cluster withhigh spending but low product diversityis a plausible and meaningful business insight: it may representloyal customers who repeatedly purchase a narrow range of products. The auditor should therefore recommend treating this cluster as apotentially valid segment(B), subject to further business analysis and controls where appropriate.
Option A is incorrect because this pattern does not imply algorithm failure. Option C (adding more clusters) might overcomplicate the segmentation without evidence that the current clustering is deficient. Option D misunderstands the purpose of clustering; a supervised model would require labeled outcomes and is not necessarily "more accurate" for exploratory segmentation. AAIA's content on AI in audit processes stresses that auditors must interpret AI-driven insights critically, not assume anomalies equal errors.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes (AI tools, use of clustering and analytics in audit).
ISACA analytics guidance on interpreting unsupervised learning outcomes in an audit context.

NEW QUESTION # 27
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