Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You work for a hospital that wants to optimize how it schedules operations to ensure that bed usage is efficiently managed. To achieve this, you need to create a machine learning model that can predict the number of beds needed for patients each day based on the number of scheduled surgeries. The hospital has provided one year of historical data, organized in 365 rows, with the following variables for each day: • Number of scheduled surgeries • Number of beds occupied • Date (to capture temporal patterns) Given the requirement to maximize the speed of model development and testing, which method should you choose to build this predictive model?
A
Create a BigQuery table. Use BigQuery ML to build a regression model, with number of beds as the target variable, and number of scheduled surgeries and date features (such as day of week) as the predictors.
B
Create a BigQuery table. Use BigQuery ML to build an ARIMA model, with number of beds as the target variable, and date as the time variable.
C
Create a Vertex AI tabular dataset. Train an AutoML regression model, with number of beds as the target variable, and number of scheduled minor surgeries and date features (such as day of the week) as the predictors.
D
Create a Vertex AI tabular dataset. Train a Vertex AI AutoML Forecasting model, with number of beds as the target variable, number of scheduled surgeries as a covariate and date as the time variable.