Google Professional Machine Learning Engineer
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As an ML engineer at a bank tasked with reducing loan defaults using AI, you have access to labeled historical loan default data in BigQuery. For compliance, you must provide explanations for any loan rejections. What is your recommended course of action?
A
Import the historic loan default data into AutoML. Train and deploy a linear regression model to predict default probability. Report the probability of default for each loan application.
B
Create a custom application that uses the Gemini large language model (LLM). Provide the historic data as context to the model, and prompt the model to predict customer defaults. Report the prediction and explanation provided by the LLM for each loan application.
C
Train and deploy a BigQuery ML classification model trained on historic loan default data. Enable feature-based explanations for each prediction. Report the prediction, probability of default, and feature attributions for each loan application.
D
Load the historic loan default data into a Vertex AI Workbench instance. Train a deep learning classification model using TensorFlow to predict loan default. Run inference for each loan application, and report the predictions.