Free 365 Days Exam Updates AAIA dumps with test Engine Practice [Q130-Q144]

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Free 365 Days Exam Updates AAIA dumps with test Engine Practice

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ISACA AAIA Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance and Risk	33%	– AI Models, Considerations, and Requirements – Ethics, Regulations, and Standards for AI – AI Governance and Program Management – Privacy and Data Governance Programs – AI Risk Management
AI Operations	46%	– Third-Party and Supply Chain Risk – Performance Monitoring and Evaluation – Operational Controls and Readiness – Incident Management and Resilience – AI System Lifecycle and Deployment
AI Auditing Tools and Techniques	21%	– Audit Evidence Collection Techniques – Data Quality and Analytics for AI Audit – Audit Planning and Design – AI Audit Outputs and Reporting – Audit Testing and Sampling Methodologies

NEW QUESTION 130
What is the MOST important reason government organizations should provide regular AI training programs for all staff?

To minimize the cost of AI deployment

To ensure staff are up to date on ethical considerations

To allow staff to understand the tools available

To reduce learning using outdated information

NEW QUESTION 131
Which of the following would provide the BEST evidence to an IS auditor that an AI model’s outputs are effectively controlled for bias?

Accuracy ranges for various demographic groups are similar.

The organization’s AI policies include a clear definition of fairness.

Model training is restricted to data containing real-world human decisions.

Technical details of model development processes are transparent.

NEW QUESTION 132
Which of the following is MOST important to have in place when initially populating data into a data frame for an AI model?

The box charts, histograms, scatterplots, and Venn diagrams that identify correlations and outliers

The code for separating data into training and testing data sets

An analysis of exploratory data that checks for incorrect data types, null values, and duplicate entries

An approved risk assessment for including, excluding, or subsequently dropping data attributes from the model

NEW QUESTION 133
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

Precision

Completeness

Accuracy

Recall

NEW QUESTION 134
Which of the following BEST describes “model drift” as distinct from “data drift”?

Model drift refers to changes in input data distribution; data drift refers to a decline in model performance

Model drift refers to a decline in model predictive performance over time; data drift refers to changes in the statistical properties of input data

They are interchangeable terms

Model drift only applies to deep learning models

NEW QUESTION 135
A financial institution ‘ s customer service chatbot sometimes gives users incorrect legal advice, leading to several customer complaints and potential regulatory exposure. Which of the following should be done FIRST to mitigate this risk?

Expand the chatbot ‘ s training dataset.

Retrain the chatbot on a more diverse dataset.

Introduce a human-in-the-loop review process.

Review chatbot technical documentation.

NEW QUESTION 136
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?

Student Number field in integer format

Grade Level field in float format

Final Grade Percent field in object format

Having an undergraduate degree in Boolean format

NEW QUESTION 137
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

NEW QUESTION 138
An IS auditor is reviewing an AI application that uses customer data to refine the organization’s marketing outreach strategies. Which of the following should be the auditor’s PRIMARY focus during this review?

Alignment with organizational AI strategies

Access control design and effectiveness

Compliance with applicable privacy regulations

Escalation protocols for sensitive data breaches

NEW QUESTION 139
Which of the following is the PRIMARY purpose of an AI acceptable use policy?

Establishing guidance on the ethical use of AI

Outlining AI usage monitoring procedures

Educating employees on where to find and how to use AI tools

Explaining the distinction between different types of AI

NEW QUESTION 140
Which of the following is MOST important to consider when deciding whether to implement an AI solution?

The cost of AI implementation

The speed of AI implementation

The space required for AI hardware

The ethical implications of AI

NEW QUESTION 141
An organization seeks to sustain effective AI governance and risk management amid rapidly evolving AI technologies. Which of the following represents the MOST effective course of action?

Provide role-specific AI training to technical staff.

Outsource AI training to external vendors.

Conduct comprehensive AI training for senior management.

Integrate continuous AI training into security awareness programs.

NEW QUESTION 142
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?

Intellectual property leakage and invalidation

Benchmarking against peer organizations

Incident response plan

Business disruption and financial impact

NEW QUESTION 143
An organization plans to share customer data collected through an AI system with third-party vendors. Which of the following BEST demonstrates compliance with data privacy principles?

Including a statement about AI data sharing practices in the company’s privacy policy

Obtaining expressed consent from customers before sharing their data

Communicating to customers about AI data sharing practices

Ensuring vendors implement adequate technical safeguards for data protection

NEW QUESTION 144
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card – Electrical Grid Predictive Maintenance Model
Model Information:
Description: AI model designed to predict maintenance needs for
electrical grid components, reduce unplanned downtime, and improve grid reliability.
Inputs: Real-time sensor data, historical maintenance records, and
operational logs.
Outputs: Maintenance needs predictions for 60 & 90 days.
Evaluation:
Approach: Cross-validation and validation of accuracy, precision, and
recall.
Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%

The AI model correctly predicts maintenance needs 95% of the time.

The electrical grid uptime is expected to be 72% of the time.

Grid failure is predicted to occur after 90 days.

F1 indicates that the model identifies true maintenance needs 76% of the time.

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Free 365 Days Exam Updates AAIA dumps with test Engine Practice [Q130-Q144]

ISACA AAIA Exam Syllabus Topics:

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