PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #319
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #319: Real Exam Question with Answer & Explanation
The correct answer is C: Train and deploy a BigQuery ML classification model trained on historic loan default data. Enable. BigQuery ML supports training classification models directly on data stored in BigQuery and provides built-in support for feature-based explanations using ML.EXPLAIN_PREDICT. This satisfies the compliance requirement for explainability while enabling accurate predictions and seam
Question
You are an ML engineer at a bank. The bank's leadership team wants to reduce the number of loan defaults. The bank has labeled historic data about loan defaults stored in BigQuery. You have been asked to use AI to support the loan application process. For compliance reasons, you need to provide explanations for loan rejections. What should you do?
Options
- AImport the historic loan default data into AutoML. Train and deploy a linear regression model to
- BCreate a custom application that uses the Gemini large language model (LLM). Provide the historic
- CTrain and deploy a BigQuery ML classification model trained on historic loan default data. Enable
- DLoad the historic loan default data into a Vertex AI Workbench instance. Train a deep learning
Explanation
BigQuery ML supports training classification models directly on data stored in BigQuery and provides built-in support for feature-based explanations using ML.EXPLAIN_PREDICT. This satisfies the compliance requirement for explainability while enabling accurate predictions and seamless integration with existing data infrastructure.
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