Professional Machine Learning Engineer Exam - Question 207

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?

Examice · Accepted Answer

To predict the daily bed occupancy based on the number of scheduled surgeries, it's important to use a model that can handle the time-series aspect of the data, as well as the relationship between surgeries and bed usage. Vertex AI AutoML Forecasting is designed to handle multivariate time-series data, where 'date' can be used as the time variable and the 'number of scheduled surgeries' can be used as a covariate. This will allow the model to account for temporal patterns and directly incorporate the effect of the surgeries on bed occupancy, thereby maximizing the speed and accuracy of model development and testing.

shadz10 · Answer

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

b1a8fae · Answer

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 · Answer

D is correct I believe

Yan_X · Answer

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

kalle_balle · Answer

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

sonicclasps · Answer

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

CHARLIE2108 · Answer

I went with B.

pinimichele01 · Answer

best suited

fitri001 · Answer

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.

b2aaace · Answer

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.

info_appsatori · Answer

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.

dija123 · Answer

Train a Vertex AI AutoML Forecasting model

VinaoSilva · Answer

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

Professional Machine Learning Engineer Exam - Question 207

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