Exam Professional Machine Learning Engineer topic 1 question 215 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 215
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

A. Use Prophet on Vertex AI Training to build a custom model.
B. Use Vertex AI Forecast to build a NN-based model.
C. Use BigQuery ML to build a statistical ARIMA_PLUS model.
D. Use TensorFlow on Vertex AI Training to build a custom model.

Show Suggested Answer

Suggested Answer: C 🗳️

by pikachu007 at Jan. 13, 2024, 6:02 a.m.

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fitri001

9 months, 4 weeks ago

Selected Answer: C

Quick Implementation: BigQuery ML simplifies the process. You can train and deploy the model directly within BigQuery, eliminating the need for complex model deployment or data movement. Minimal Effort: ARIMA_PLUS is a pre-built statistical model available in BigQuery ML. You don't need to write custom code for a complex neural network (NN) model like in option B or D. Time Series Data: ARIMA models are well-suited for time series forecasting, which is ideal for your monthly sales prediction task.

upvoted 2 times

fitri001

9 months, 4 weeks ago

why not others? A. Prophet on Vertex AI Training: While Prophet is a good choice for time series forecasting with holidays and seasonality, using Vertex AI Training requires additional setup and potentially custom code compared to the readily available ARIMA_PLUS model within BigQuery ML. B. Vertex AI Forecast with NN-based Model: Building a custom NN-based model using Vertex AI Forecast offers flexibility but requires more effort and expertise in model development and potentially hyperparameter tuning. This might not be ideal for a quick implementation. D. TensorFlow on Vertex AI Training: Similar to option B, using TensorFlow for a custom model offers flexibility but requires significant coding and expertise, making it less suitable for a quick and low-effort approach.

upvoted 1 times

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pinimichele01

10 months, 1 week ago

Selected Answer: C

data on bigquery + minimal effort -> C

upvoted 1 times

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b1a8fae

1 year ago

Selected Answer: C

Given amount of data (few thousand short-cycled products) and frequency of predictions (monthly) C is the way to go.

upvoted 1 times

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pikachu007

1 year, 1 month ago

Selected Answer: C

Ease of Use: BigQuery ML integrates seamlessly with BigQuery, allowing you to create and train models directly within SQL queries, eliminating the need for separate environments or coding. Statistical ARIMA_PLUS Strengths: This model is well-suited for time series forecasting, automatically handling seasonality, trends, and holidays, making it appropriate for monthly sales predictions. Minimal Effort: BigQuery ML handles model training and tuning, reducing the need for manual configuration or hyperparameter tuning. Fast Implementation: Model creation and training can be done in a few lines of SQL, enabling rapid deployment.

upvoted 4 times

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