Download ISACA Advanced in AI Audit.AAIA.ExamTopics.2026-03-20.69q.tqb

Vendor: ISACA
Exam Code: AAIA
Exam Name: ISACA Advanced in AI Audit
Date: Mar 20, 2026
File Size: 223 KB
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Demo Questions

Question 1
Which of the following is MOST important to consider when auditing an organization’s AI procedures?
  1. AI data validation and filtration to prevent data poisoning
  2. Backup and recovery in the event of an AI data breach
  3. Employee training on recognized AI best practices
  4. Frequency of AI system updates to enhance security
Correct answer: A
Question 2
Which of the following is the MOST important key indicator to assess when auditing the deployment of AI in predictive maintenance system in manufacturing operations?
  1. Percentage of AI’s predictive algorithms that are compliant with industry standards
  2. Frequency of updates to AI predictive models based on equipment usage
  3. Reduction in downtime and maintenance costs due to AI use
  4. Total number of predictive maintenance scenarios implemented using AI
Correct answer: C
Question 3
A logistics company uses an AI model to optimize delivery routes. Which of the following methods would BEST verify the model’s accuracy?
  1. Using advanced outlier detection techniques to identify anomalies in route length
  2. Running bias tests to confirm that the model does not favor certain delivery areas
  3. Comparing the AI-generated routes with routes generated using traditional methods
  4. Testing the model on the most recent data for the latest conditions
Correct answer: D
Question 4
Which of the following controls would BEST minimize the risk of bias introduced into AI models from flawed training data?
  1. Implementing enhanced access controls over AI model training data
  2. Testing AI models using adversarial methods
  3. Separating AI training and production data
  4. Performing cross-validation of AI models
Correct answer: D
Question 5
Which of the following metrics should an IS auditor recommend as a key performance indicator (KPI) to assess the reliability of a natural language processing AI model?
  1. Landmark localization error
  2. Receiver operating characteristic curve
  3. Recall-oriented understudy for gisting evaluations
  4. Mean squared error
Correct answer: C
Question 6
Which of the following is the MOST critical consideration for incident response in AI systems?
  1. Developing a process to monitor and correct biased outputs in AI models
  2. Assigning accountability for managing and resolving AI -related incidents
  3. Deploying automated incident response systems to flag unusual patterns in AI model behavior
  4. Conducting regular training to enhance understanding of AI troubleshooting
Correct answer: B
Question 7
Post-deployment, anomalies in logs suggest a conflict between a critical security patch and an AI model's inference engine. Which of the following is the BEST course of action?
  1. Investigate the patch’s effect on the model.
  2. Contact the patch vendor for support.
  3. Reapply the patch and test the engine again.
  4. Restart the inference engine to clear conflicts.
Correct answer: A
Question 8
When evaluating the process flowchart of an AI machine learning (ML) model, which task should be performed FIRST to provide assurance that the model will function as intended?
  1. Scale and standardize numerical variables.
  2. Split the data set into training and test sets.
  3. Apply the model algorithm.
  4. Address imbalances in the data.
Correct answer: B
Question 9
A model developer is looking to mitigate the risk of information loss within an imbalanced data set that contains majority and minority classes. Which of the following strategies would an IS auditor consider to be MOST appropriate?
  1. Undersampling to remove excess data points
  2. Splitting data into к-folds with cross-validation
  3. Tuning the model’s hyperparameters
  4. Oversampling to create new data points
Correct answer: D
Question 10
Which of the following BEST improves predictions and understanding to refine the accuracy of AI models?
  1. Data augmentation
  2. Mean clustering
  3. Prompt templates
  4. Data compression
Correct answer: A
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