Google Professional Machine Learning Engineer Question 6
Single answerGoogle Cloud PlatformYou are working with a marketing dataset in BigQuery that contains customer details and their responses to a promotional email. The dataset includes columns such as 'age', 'gender', 'region', 'income', and 'responded_to_email' (1 for yes, 0 for no). Your task is to predict whether a customer will respond to a promotional email based on these features. Which BigQuery ML model is most appropriate for this use case?
- A
Linear Regression
- B
Binary Logistic Regression
- C
K-means Clustering
- D
Matrix Factorization
Show answer and explanation
Correct answer: B
Explanation
The goal in this scenario is to predict a binary outcome (whether a customer will respond to a promotional email or not) based on various features. Binary Logistic Regression is the most suitable model in BigQuery ML for solving this type of binary classification problem. Other options like Linear Regression, K-means Clustering, and Matrix Factorization are designed for different use cases and are not appropriate here.
- A. Incorrect.
Linear Regression is used for predicting continuous numerical values, such as revenue or temperature. It is not suitable for binary classification tasks like predicting a yes/no response.
- B. Correct.
Binary Logistic Regression is designed for binary classification problems, where the target variable has two possible outcomes (e.g., 1 for yes and 0 for no). This makes it the most appropriate choice for this scenario.
- C. Incorrect.
K-means Clustering is an unsupervised learning model used for grouping data points into clusters. It does not perform predictive tasks like classification.
- D. Incorrect.
Matrix Factorization is typically used for recommendation systems, where the goal is to predict missing values in a user-item interaction matrix. It is not suitable for binary classification tasks.