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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #218

You work for a hospital that wants to optimize how it schedules operations. You need to create a model that uses the relationship between the number of surgeries scheduled and beds used. You want to p

The correct answer is D. Create a Vertex AI tabular dataset. Train a Vertex AI AutoML Forecasting model, with number of. To maximize the speed of model development and testing for predicting daily bed occupancy based on scheduled surgeries, the data should be prepared as a Vertex AI tabular dataset and an AutoML Forecasting model should be trained.

Submitted by luis.pe· Apr 18, 2026ML model development

Question

You work for a hospital that wants to optimize how it schedules operations. You need to create a model that uses the relationship between the number of surgeries scheduled and beds used. You want to predict how many beds will be needed for patients each day in advance based on the scheduled surgeries. You have one year of data for the hospital organized in 365 rows. The data includes the following variables for each day: - Number of scheduled surgeries - Number of beds occupied - Date You want to maximize the speed of model development and testing. What should you do?

Options

  • ACreate a BigQuery table. Use BigQuery ML to build a regression model, with number of beds as
  • BCreate a BigQuery table. Use BigQuery ML to build an ARIMA model, with number of beds as the
  • CCreate a Vertex AI tabular dataset. Train an AutoML regression model, with number of beds as
  • DCreate a Vertex AI tabular dataset. Train a Vertex AI AutoML Forecasting model, with number of

How the community answered

(31 responses)
  • A
    6% (2)
  • B
    3% (1)
  • C
    13% (4)
  • D
    77% (24)

Why each option

To maximize the speed of model development and testing for predicting daily bed occupancy based on scheduled surgeries, the data should be prepared as a Vertex AI tabular dataset and an AutoML Forecasting model should be trained.

ACreate a BigQuery table. Use BigQuery ML to build a regression model, with number of beds as

A standard BigQuery ML regression model is not inherently optimized for time-series forecasting, requiring manual feature engineering to properly account for temporal dependencies, which goes against maximizing development speed for this type of problem.

BCreate a BigQuery table. Use BigQuery ML to build an ARIMA model, with number of beds as the

While BigQuery ML does support ARIMA models for time-series forecasting, Vertex AI AutoML Forecasting typically offers a broader range of advanced time-series models and more automation, leading to potentially faster development and testing for complex scenarios with exogenous variables.

CCreate a Vertex AI tabular dataset. Train an AutoML regression model, with number of beds as

A general Vertex AI AutoML regression model, while efficient for tabular data, is not specialized for time-series forecasting; it would require manual preparation of time-based features to handle the temporal dependencies adequately, which adds development effort.

DCreate a Vertex AI tabular dataset. Train a Vertex AI AutoML Forecasting model, with number ofCorrect

The problem is a time-series forecasting task to predict future bed needs based on historical data and scheduled surgeries. Vertex AI AutoML Forecasting is specifically designed for such scenarios, automating the process of building and testing time-series models with a focus on maximizing development speed by handling time-series specific challenges and allowing for exogenous variables.

Concept tested: Time-series forecasting with AutoML

Source: https://cloud.google.com/vertex-ai/docs/tabular-data/model-types/time-series-forecasting

Topics

#Time Series#Forecasting#AutoML#Vertex AI

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