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  2. ISACA Certification
  3. AAIA Exam
  4. ISACA.AAIA.v2026-06-21.q153 Dumps
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Question 121

An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?

Correct Answer: B
In a proper AI pipeline, the original data must be "strictly partitioned" into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were "Reused" across sets.
This leads to "Data Leakage," where the model "sees" the test or validation data during the training phase. As a result, the model's performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIATM Study Guide identifies data leakage as a primary cause of "Model Evaluation Failure," rendering all testing results invalid.
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Question 122

Which of the following AI documents would support an IS auditor assessing hyperparameter tuning records?

Correct Answer: B
Hyperparameter tuning involves adjusting settings like learning rate, batch size, or layer depth to optimize performance. This process is documented in "Model development logs," which record the different "experiments" run by data scientists, the parameters used in each, and the resulting performance metrics (e.g., accuracy or loss). For an auditor, these logs are the "Audit Trail" of the model's construction.
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Question 123

Which of the following is the MOST important reason for measuring the AI system's performance against predefined metrics in a staging environment?

Correct Answer: C
The AAIATM Study Guide emphasizes the importance of a rigorous "Staging" or "User Acceptance Testing" (UAT) phase in the AI lifecycle. Measuring performance against predefined metrics (such as Precision, Recall, or F1-Score) in a non-production environment is critical to
"Validate that the system meets business and technical requirements prior to release". This step prevents the deployment of models that may exhibit bias, inaccuracy, or logic errors in the real world.
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Question 124

Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?

Correct Answer: A
Bias in AI models is most commonly introduced through the training data. The AAIATM Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results."
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Question 125

Which of the following is the MOST effective control to prevent data poisoning attacks during model training?

Correct Answer: B
Data poisoning involves injecting malicious or mislabeled data into the training set. Provenance verification and data validation pipelines reduce the likelihood of compromised data reaching the model.
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