Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 236 discussion

Exam Professional Machine Learning Engineer topic 1 question 236 discussion - ExamTopics

AI Summary Hide AI Generated Summary

Problem Description

The problem involves building a model to predict customer churn for a large retailer using a dataset containing customer demographics, purchase history, and website activity. The model needs to be created in BigQuery ML and its performance thoroughly evaluated.

Options

A: Create a linear regression model in BigQuery ML, register it in Vertex AI Model Registry, and evaluate performance in Vertex AI.
B: Create a logistic regression model in BigQuery ML, register it in Vertex AI Model Registry, and evaluate performance in Vertex AI.
C: Create a linear regression model in BigQuery ML and use the ML.EVALUATE function for evaluation.
D: Create a logistic regression model in BigQuery ML and use the ML.CONFUSION_MATRIX function for evaluation.

Suggested Answer

The suggested answer is B, which involves creating a logistic regression model (suitable for binary classification problems like churn prediction) in BigQuery ML and evaluating its performance using Vertex AI.

You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

A. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
B. Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
C. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.

Show Suggested Answer Hide Answer