MLS-C01 Question #176: Real Exam Question with Answer & Explanation

Question

A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made. The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days. The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science. What should the data science team do to address this issue in the MOST operationally efficient manner?

Options

• AUse Amazon SageMaker Studio to rebuild the model.
• BUse Amazon SageMaker Studio to rebuild the model.
• CCreate an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost
• DUse Amazon SageMaker Studio to rebuild the model.

Explanation

https://aws.amazon.com/blogs/machine-learning/ml-explainability-with-amazon-sagemaker-

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