How to Forecast Daily Bed Usage Using Scheduled Surgeries?

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?

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.
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.
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.
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. Source Reference Answer

Community Votes

68%

32%

68% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

It tests recognizing a time-series forecasting scenario with external regressors, where the common trap is defaulting to standard regression due to the small dataset size and perceived simplicity.

This question tests selecting the correct Vertex AI AutoML Forecasting model for time-series prediction with external covariates. The community consensus favors Option D because it correctly treats daily bed occupancy as a forecasting task while incorporating scheduled surgeries as predictive features.

Option A (BigQuery ML Regression) is frequently chosen because candidates prioritize development speed and assume a simple linear relationship, overlooking that daily predictions over time require a forecasting approach that accounts for temporal patterns and future covariate inputs.

Community Discussion (17 comments)

NamitSehgal 👍 1 Selected: D

A (BigQuery ML Regression): A simple regression model is not designed for time series forecasting. It wouldn't capture the temporal dependencies in the data and wouldn't be able to effectively predict future bed usage based on past trends. D Forecasting Task

Hrishikesh1992 👍 1 Selected: B

We have only 360 rows of data, I went with it because we require statistical model here rather than ML models.

lunalongo 👍 2 Selected: A

A is the best option because: - BigQuery ML allows model build/training within BigQuery using SQL - This is a regression model, not a timeseries forecast; no ARIMA (B) fit! - Data transfer to Vertex AI (C, D) and usage of AutoML not needed - AutoML is better for larger datasets, BigQuery ML works for 365 rows

forport 👍 3 Selected: D

'Vertex AI AutoML Forecasting' == for forecasting time series data

VinaoSilva 👍 2 Selected: D

"You want to predict how many beds will be needed for patients each day" = Forecasting

dija123 👍 1 Selected: D

Train a Vertex AI AutoML Forecasting model

info_appsatori 👍 1 Selected: A

IDK, i going with A, because its maximize the speed of development and testing. Also in question it says: You need to create a model that uses the """relationship"" between the number of surgeries scheduled and beds used. = linear regression problem.

b2aaace 👍 1 Selected: C

I don't think this is a time series forecasting problem. The question clearly states that we should predict the number of beds based on the number of scheduled surgeries. this is a simple linear regression problem.

fitri001 👍 2 Selected: D

Vertex AI AutoML Forecasting: This option leverages Vertex AI's AutoML capabilities for time series forecasting. It automatically explores different model types and hyperparameters to find the best fit for your data. This can significantly speed up model development compared to building a model from scratch. Date as time variable, surgeries as covariate: This approach acknowledges the time-series nature of bed occupancy with "date" as the time series variable. It also incorporates the "number of scheduled surgeries" as a covariate, allowing the model to learn the relationship between surgeries and bed usage.

pinimichele01 👍 1 Selected: D

best suited

CHARLIE2108 👍 1 Selected: B

I went with B.

sonicclasps 👍 1 Selected: D

best suited, and treats the input as a time series, unlike A

Yan_X 👍 2 Selected: D

D, as B doesn't mention the 'number of scheduled surgeries'.

shadz10 👍 2 Selected: D

D is correct I believe

b1a8fae 👍 4 Selected: A

A. Using BigQuery to comply requirement of speed of development. ARIMA does not use number of scheduled surgeries, and it is stated that the prediction must be based on that variable. So it must be A. LR model on BQ using scheduled surgeries, day of the week, etc, as predictors.

shadz10 👍 3

365 days of data may be insufficient for big query I’m going with C

kalle_balle 👍 1 Selected: B

Using B instead of D as it requires speed of development.

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Expert Analysis

Why the Answer Is Correct

Vertex AI AutoML Forecasting is explicitly designed for time-series problems where you predict a target variable over time using historical data and external covariates. In this scenario, "date" serves as the time column, "number of beds" is the target, and "number of scheduled surgeries" acts as an external covariate that drives future demand. AutoML automatically handles feature engineering, model selection, and hyperparameter tuning, directly satisfying the requirement to maximize development speed.

Why the Other Options Are Wrong

Option A uses standard regression, which does not natively handle temporal dependencies or generate forecasts into the future without manual lagging adjustments. Option B (ARIMA in BigQuery ML) cannot incorporate external covariates like scheduled surgeries, making it unsuitable for this causal prediction. Option C is a standard tabular regression model, which again fails to treat the problem as a sequential time-series forecast and lacks built-in forecasting capabilities for future dates.

Community Comment Notes

Several users initially chose A, arguing that 365 rows are too small for Vertex AI and that BigQuery ML offers faster iteration (Comments [1], [4], [11]). However, higher-voted responses emphasize that GCP's AutoML Forecasting supports small datasets and explicitly models time-series dynamics (Comment [2], [6]). Comment [8] correctly notes that standard regression misses temporal dependencies, while Comment [7] highlights that ARIMA lacks covariate support, reinforcing D as the only architecturally sound choice.

Official Reference

Exam Strategy

Always identify whether a problem requires forecasting (predicting future values over time) versus static regression; if dates or timestamps are central to the prediction horizon, prioritize AutoML Forecasting or dedicated time-series tools. When external drivers influence the outcome, ensure your selected model supports covariates, as pure univariate models will miss critical predictive signals.

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