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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 (Q204-Q209):

NEW QUESTION # 204
An organization deploys a complex AI model to support credit risk assessments. Stakeholders find the model' s output difficult to interpret. Which of the following BEST improves interpretability?

A. Implementing a rule-based system to validate the AI model's decisions
B. Training stakeholders to interpret AI outputs
C. Reducing the model's complexity
D. Developing documentation and visual tools explaining how the model generates outputs

Answer: D

Explanation:
AAIA emphasizes thattransparency and interpretabilityrequire clear explanations of how the model functions, what features drive predictions, and how decisions are derived.
Creating documentation and visual interpretability tools (option C) provides:
* Feature importance breakdowns
* Decision pathway visualizations
* Examples of prediction reasoning
* Plain-language explanations for nontechnical stakeholders
* Evidence that can be validated during audits
While training stakeholders (A) is helpful, it does not make the model itself clearer.
A rule-based system (B) supports validation, not interpretability.
Simplifying the model (D) may affect accuracy and is not necessary if documentation and interpretability tooling solve the issue.
Therefore, the best approach is to improve interpretability through clear documentation and visualization.
References:
AAIA Domain 5: Explainability and Transparency Controls
AAIA Domain 3: Auditability and Documentation Requirements

NEW QUESTION # 205
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

A. Performing a business impact analysis (BIA) to assess the consequences of AI model failure
B. Assessing data lineage to verify the traceability of data sources
C. Conducting a penetration test to identify vulnerabilities in the model
D. Reviewing the organization's AI software development life cycle documentation

Answer: B

NEW QUESTION # 206
A financial institution's credit-scoring model shows high accuracy overall but performs poorly for a specific demographic subgroup. What should the auditor recommend FIRST?

A. Immediately decommission the model
B. Add more features to the model
C. Increase the overall accuracy threshold
D. Investigate whether the training data underrepresents that subgroup and reassess fairness metrics

Answer: D

Explanation:
Subgroup underperformance often stems from underrepresentation in training data. Root-cause analysis should precede any remediation decision such as retraining or decommissioning.

NEW QUESTION # 207
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

A. Precision
B. Recall
C. Accuracy
D. Specificity

Answer: B

NEW QUESTION # 208
Which of the following is the BEST way to ensure data fed into an AI model aligns with business objectives?

A. Define new data attributes
B. Document the data input requirements
C. Normalize the data within expected tolerances
D. Change to new data sources

Answer: B

Explanation:
Documenting data input requirements (option C) ensures that all incoming data supports the business purpose, operational constraints, and intended use cases of the AI model.
AAIA highlights that aligning AI systems with business objectives starts withclear data specifications, including:
* Required fields and data formats
* Data quality thresholds
* Acceptable ranges and constraints
* Mandatory attributes
* Source system definitions
* Business rationale for each feature
Without documentation, data pipelines may ingest irrelevant, low-quality, or misaligned data, causing the model to drift away from business needs.
Normalization (A) improves preprocessing but does not ensure alignment.
Switching data sources (B) is premature without evaluating needs.
Defining new attributes (D) is secondary to documenting overall requirements.
References:
AAIA Domain 1: Business Alignment and Data Requirements
AAIA Domain 2: Input Specification Governance

NEW QUESTION # 209
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