CyberCertPrepAI Security FundamentalsData Security & Privacy in AIEASYFree question
A university lecturer teaches about "data minimization in AI feature design." Which of the following is the best application of this principle when building a loan-underwriting AI?
- A.Minimize data storage costs by compressing all training data with lossless compression
- B.Use all available customer data and let the model learn which features are predictively useful during training
- C.Collect only features demonstrably necessary for credit risk prediction (e.g., repayment history, debt-to-income ratio) and exclude features that are not predictively necessary (e.g., social media activity, shopping preferences)
- D.Minimize the number of model parameters to reduce inference computation costs
Why C is correct
Data minimization in AI feature design means identifying and using only the features genuinely necessary for the stated purpose. For credit underwriting, demonstrably relevant financial behavior indicators are appropriate; extraneous behavioral data that is not necessary for credit prediction violates minimization even if it might marginally improve accuracy. Collecting all available data and letting the model decide violates minimization. Storage compression and parameter minimization are engineering concerns unrelated to GDPR data minimization.
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