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Question 282 - Professional Machine Learning Engineer discussion

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You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?

A.

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.

Answers
A.

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.

B.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an ARIMA model.

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B.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an ARIMA model.

C.

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.

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C.

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.

D.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.

Answers
D.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.

Suggested answer: C

Explanation:

BigQuery ML allows you to build and run machine learning models using SQL queries directly within BigQuery, which is one of the simplest approaches because it doesn't require setting up an external environment like Vertex AI or managing infrastructure.

AutoML regression is more appropriate for predicting customer lifetime value (CLV) compared to ARIMA, which is typically used for time series forecasting (e.g., sales over time, stock prices, etc.). CLV prediction involves understanding complex relationships between customer behavior and value, which is best captured by a regression model.

Using BigQuery Studio and running a CREATE MODEL statement to build an AutoML regression model offers the simplicity you're looking for because it automates much of the feature engineering, model selection, and hyperparameter tuning.

The other options involving ARIMA models (A and B) are not appropriate for CLV, and setting up a Vertex AI Workbench notebook (D) introduces unnecessary complexity for this task.

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asked 07/11/2024
Cristi Savin
50 questions
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