The correct answer is Emphasize ethical practices by documenting preprocessing steps and preferring interpretable models.

Emphasize ethical practices by documenting preprocessing steps and preferring interpretable models is correct because documenting preprocessing creates transparency and reproducibility, and it makes it easier to audit data transformations for bias or errors. Preferring interpretable models helps stakeholders and regulators understand predictions, and it supports fair lending practices and actionable remediation when issues are found.

Practical evaluation under this approach includes multiple performance metrics, representative test sets, and fairness checks rather than relying on a single number. It also includes human review and clear records of how data was prepared, so that model behavior can be investigated and explained.

Rely only on accuracy metrics and ignore input preprocessing is wrong because accuracy alone can hide class imbalance and fairness problems, and ignoring preprocessing prevents detection of biased or corrupted inputs.

Choose highly complex neural networks without evaluating interpretability is wrong because highly complex models may perform well but they are often hard to explain, and that lack of interpretability is a poor fit for regulated lending decisions where explanations and accountability are required.

Cloud AutoML is a less suitable choice in this context and it is also an older branding that has largely been consolidated into Vertex AI AutoML. Automated tools can help, but using them without explicit documentation, interpretability checks, and fairness evaluation does not meet responsible AI practices and references to legacy services may be less likely on newer exams.

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