Free 2025 Advanced in AI Audit AAIA dumps are available by Real4Prep [Q21-Q39]

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Free 2025 Advanced in AI Audit AAIA dumps are available on Google Drive shared by Real4Prep

Welcome to download the newest Real4Prep AAIA PDF dumps: https://www.real4prep.com/AAIA-exam.html ( 93 Q&As)

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.

NO.21 A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model’s decisions to stakeholders?

Using neural network visualization to show how the AI model processes data through its layers

Using K-means algorithms to group vehicles based on mileage or engine temperature for maintenance patterns

Utilizing support vector machines (SVM) to classify vehicles based on maintenance urgency

Using local interpretable model-agnostic explanation (LIME) to analyze how specific features contribute to predictions

NO.22 Which of the following AI system characteristics would BEST help an IS auditor evaluate the system’s algorithm?

The AI system algorithm uses training data to inform decision output.

The AI system provides multiple options for model training.

The AI system provides transparent justification of decisions.

The AI system uses archived transaction data to provide decisions.

NO.23 When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

Precision

Completeness

Accuracy

Recall

NO.24 An organization is adopting AI for its procurement and inventory teams, raising concern from stakeholders that they will lose their jobs due to AI. Which of the following is the BEST way for the IS auditor to assess whether the potential negative impacts were minimized?

Review human-centered design practices to determine how they were considered.

Review the AI roadmap for short-term and long-term milestones.

Review how the project management team collected feedback in engagement activities.

Review the current state assessment of how AI may impact the organization.

NO.25 In the context of an AI implementation, which of the following actions is MOST critical for an organization’s change management program?

Ensuring the organization has a dedicated AI governance committee

Reviewing documentation for AI system changes, updates, and patches

Conducting a comprehensive risk assessment specific to AI-related changes

Verifying that all employees have completed mandatory AI ethics training

NO.26 During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization’s risk tolerance. Which of the following is the MOST appropriate response?

Postpone deployment until the risk can be safely managed.

Enhance the data that the model is trained on.

Obtain board approval for an exception.

Revisit the risk tolerance to ensure it is appropriate.

NO.27 When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?

Robotic process automation (RPA)

Autoregressive sequencing model

Predictive analytics

Natural language processing

NO.28 An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?

The AI will process only a portion of the data set.

The AI will prioritize high-value entries.

The AI will reject the data set and not analyze the data.

The AI output will be biased toward the first 3,000 tokens.

NO.29 When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?

Inability to identify where an AI system is housed

System output not being checked for inconsistencies

Cascading failures of AI system outputs

Difficulty of documenting AI algorithm processes

NO.30 An IS auditor is auditing an organization’s data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor’s MOST important consideration?

Retention of stored data

Portability of data

Data practices for training models

Accountability for data management

NO.31 Which of the following metrics are the BEST indication of a mature and effective approach to an organization’s data governance program for its AI systems?

Number of AI projects completed within the last fiscal year

Percentage of AI models with documented data lineage

Frequency of data quality audits on the organization’s data sets

Total budget allocated to AI initiatives across all departments

NO.32 An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?

Whether the training, validation, and testing data sets were created in the correct order

Whether data leakage occurred from utilizing overlapping records in the data sets

Whether a sufficient number of records were utilized in the training data set

Whether the validation data set utilized the same number of records as the training data sets

NO.33 To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:

Analyzing system metadata.

Testing the model with a curated sample data set.

Interviewing developers.

Observing the system’s interactions with end users.

NO.34 Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?

Data performance metrics

Data usage agreements

Use of open-source intellectual property

Model runtime efficiency logs

NO.35 An IS auditor notes that an AI model achieved significantly better results on training data than on test dat a. Which of the following problems with the model has the IS auditor identified?

Underfitting

Overfitting

Generalization

Bias

NO.36 Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

Identifying separation of duties conflicts for database data changes

Developing architecture diagrams

Identifying technology-specific risk and considerations

Summarizing meeting transcripts from interviews with database administrators (DBAs)

NO.37 A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations. Which of the following MOST effectively detects data drift related to the model’s predictions?

Comparing incoming patient data distributions with the training data set

Applying overrides to allow healthcare professionals to correct the AI model’s recommendations

Conducting periodic model retraining to ensure alignment with updated patient data

Using adversarial testing to simulate scenarios that stress test the model’s predictions

NO.38 The PRIMARY objective of machine learning (ML) in data processing is to:

Analyze data sets to identify visual patterns and trends.

Enhance the explainability of AI model outputs.

Perform actions that would typically require human intelligence.

Draw statistical inferences for creating artificial human intelligence.

NO.39 A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?

Limited data set diversity impacting model training

Data drift impacting system forecasting

Overfitting issues due to a small training data set

Lack of outlier checks in data affecting forecast accuracy

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