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Google Professional Machine Learning Engineer Exam - Topic 8 Question 65 Discussion

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 rowsThe data includes the following variables for each day* Number of scheduled surgeries* Number of beds occupied* DateYou want to maximize the speed of model development and testing What should you do?
D) Create a Vertex Al tabular dataset Train a Vertex Al AutoML Forecasting model with number of beds as the target variable, number of scheduled surgeries as a covariate, and date as the time variable.
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 Al tabular dataset Tram 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

Google Professional Machine Learning Engineer Exam - Topic 8 Question 65 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 65
Topic #: 8
[All Professional Machine Learning Engineer Questions]

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?

Show Suggested Answer Hide Answer
Suggested Answer: D

This approach would allow you to keep the critical columns of data while reducing the sensitivity of the dataset by removing the personally identifiable information (PII) before training the model. By creating an authorized view of the data, you can ensure that sensitive values cannot be accessed by unauthorized individuals.


by Adell at Oct 08, 2023, 06:02 PM

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Tran
9 months ago
Wait, can we really predict beds just from surgeries? Seems off.
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Laticia
10 months ago
Totally agree with A, regression models are fast!
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Ceola
10 months ago
C sounds interesting, but not sure about the minor surgeries part.
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Cammy
10 months ago
I think D might be better for forecasting.
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Alida
10 months ago
A is the way to go for quick results!
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Dominque
10 months ago
I feel like option D might be the best choice since it combines forecasting with the surgeries as a covariate, but I need to double-check how that impacts the predictions.
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Mabel
11 months ago
I practiced with AutoML before, and I think option C could work well since it allows for quick model training, but I'm unsure about the specifics of the features.
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Lynda
11 months ago
I'm not entirely sure, but I think ARIMA models are more suited for time series data, which makes option B a bit tempting, though it might not consider the surgeries directly.
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Josue
11 months ago
I remember we discussed using regression models for predicting continuous variables like bed occupancy, so option A seems like a good fit.
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Mitzie
11 months ago
I'm feeling pretty confident about this one. I think Option A is the way to go - it's the most straightforward approach and should get us a working model quickly. The date features will help capture any seasonal patterns in the data.
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Cecilia
11 months ago
Hmm, I'm a bit confused by the time series aspect of this. Should we be using an ARIMA model instead? Option B seems like it might be a better fit since we have the date information.
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Colene
11 months ago
This looks like a classic regression problem to predict the number of beds needed based on the number of scheduled surgeries. I think Option A using BigQuery ML would be the fastest approach to get a working model.
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Catrice
11 months ago
I'm not as familiar with BigQuery ML, but the AutoML options in Option C and D sound interesting. The Vertex AI Forecasting model in Option D could be a good way to handle the time series element.
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Mariko
11 months ago
Hmm, I'm a bit unsure about the aggregation and next-hop-self commands. I'll need to double-check the BGP best practices to make sure I understand how those work.
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Brandon
11 months ago
This feels similar to a question we answered about network configurations. I want to say it's 30 shelves, but I could be mixing it up.
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Sue
11 months ago
Hmm, I'm a little unsure about this one. I'll have to think through the different chart types and how they're used.
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Marvel
11 months ago
This seems like a straightforward question about the purpose of security portfolio assessments. I'll need to carefully consider each option and think about the key objectives of these assessments.
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Mirta
11 months ago
I remember similar questions on baseline conditions, and I think options like "stable" can sometimes be tricky.
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Laurel
1 year ago
Yeah, no need to overcomplicate things. BigQuery ML is the clear winner here. Get those bed predictions cranking in no time!
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Ludivina
1 year ago
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.
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Hana
1 year ago
Yeah, BigQuery ML is definitely the way to go for quick and accurate predictions.
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Emogene
1 year ago
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
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Hailey
1 year ago
Ha! Vertex AI, really? That's overkill for this simple use case. BigQuery ML is perfect - fast and easy.
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Solange
1 year ago
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.
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Erasmo
1 year ago
Ha! Vertex AI, really? That's overkill for this simple use case. BigQuery ML is perfect - fast and easy.
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Shay
1 year ago
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
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Sherrell
1 year ago
I agree, using BigQuery ML is the easiest approach. I like how it handles the data prep and model training for us.
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Jess
1 year ago
I agree, using BigQuery ML is the easiest approach. I like how it handles the data prep and model training for us.
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Lavera
1 year ago
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
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Matilda
1 year ago
BigQuery ML seems like the way to go here. I can quickly build a regression model and get predictions without having to worry about the infrastructure.
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Cyril
1 year ago
User 4: Let's go with BigQuery ML then, it seems like the most practical solution for our hospital's needs.
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King
1 year ago
User 3: I think creating a BigQuery table and using BigQuery ML is the best option for predicting bed usage based on scheduled surgeries.
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Denna
1 year ago
User 2: Definitely, it will save us time and make the process more efficient.
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Carey
1 year ago
User 1: I agree, using BigQuery ML for regression modeling is a good choice for this scenario.
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Meaghan
1 year ago
I'm not sure, option D also sounds promising with the forecasting model.
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James
1 year ago
I agree with Gilberto. It seems like the most efficient way to predict bed usage.
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Gilberto
1 year ago
I think we should go with option A, using BigQuery ML for regression modeling.
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