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Google Professional Machine Learning Engineer: Choose the Right Regression Model and Platform for Industrial Power Consumption Prediction

By: Author Alex Lim

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Learn how to select the optimal regression model and platform for estimating power consumption in industrial manufacturing plants using sensor data. Discover the advantages of using BigQuery ML for seamless scalability and minimal development effort.

Table of Contents

Question

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
B. Develop a regression model using BigQuery ML.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
D. Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.

Answer

B. Develop a regression model using BigQuery ML.

Explanation

BigQuery ML is the most suitable choice for this scenario due to several key advantages:

  1. Scalability: BigQuery ML leverages the power of BigQuery, Google Cloud’s highly scalable and fully managed data warehouse. It can handle tens of millions of records daily without requiring manual scaling or infrastructure management.
  2. Minimal development effort: BigQuery ML allows you to create and train machine learning models using SQL queries directly within BigQuery. This eliminates the need for complex data movement or additional infrastructure setup, reducing development time and effort.
  3. Seamless integration: By using BigQuery ML, you can keep your data and model training within the same platform. This ensures smooth integration between data storage, preprocessing, and model training, simplifying the overall workflow.
  4. Automatic model selection: BigQuery ML automatically selects the best model architecture based on your data and problem type. This saves time and effort in model selection and hyperparameter tuning.

While options A, C, and D involve developing custom models using TensorFlow, scikit-learn, or PyTorch and optimizing them with Vertex AI Training, they require more development effort and may not provide the same level of scalability and seamless integration as BigQuery ML for this specific use case.

Therefore, developing a regression model using BigQuery ML is the most efficient and effective approach for estimating power consumption in the company’s manufacturing plants based on the given requirements.

Google Professional Machine Learning Engineer certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Google Professional Machine Learning Engineer exam and earn Google Professional Machine Learning Engineer certification.