Question 1

Which of the following is the GREATEST risk associated with using AI in audit planning?

Options :

A : Increased planning costs

B : Scope creep

C : Incomplete data

D : Limited knowledge

Answer: C

Question 2

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?

Options :

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

B : The AI will prioritize high-value entries.

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

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

Answer: D

Question 3

A healthcare organization uses data clustering to group patients by medical history for personalized treatment recommendations. Which of the following is the GREATEST privacy risk associated with this practice?

Options :

A : The clustering requires more data, increasing the risk of a privacy breach.

B : Clustering increases the complexity of the model, making data harder to anonymize.

C : Irrelevant features in the data may result in inaccurate or biased treatments.

D : Clusters can reveal sensitive personal information depending on how the information is presented.

Answer: D

Question 4

Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

Options :

A : AI model vulnerability assessment

B : AI change management requests

C : AI model inventory

D : AI acceptable use policy

Answer: C

Question 5

An IS auditor reviewing documentation for an AI model notes that the modeler utilized a K-means clustering algorithm, which clusters data into categories for correlations and analysis. Which of the following is the MOST important risk for the auditor to consider?

Options :

A : K-means clustering is not a common data clustering method due to its complexity and difficulty categorizing data correctly.

B : K-means clustering requires the modeler to supervise the learning analysis, which can introduce bias.

C : K-means clustering algorithms are significantly sensitive to outliers and dependent on the similarity of units of measure.

D : K-means clustering determines the number of clusters for the modeler without supervision.

Answer: C