Professional Machine Learning Engineer Exam - Question 158

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?

Examice · Accepted Answer

For seamless scalability and minimal development effort, using BigQuery ML is ideal. BigQuery is designed to handle massive datasets efficiently, which aligns well with the need to process tens of millions of records daily. BigQuery ML enables you to develop and train regression models directly within the data warehouse, eliminating the need for extensive custom code. This integration facilitates straightforward scheduling of daily training runs, leveraging BigQuery's powerful processing capabilities.

Mdso · Answer

Minimal development effort => BigQueryML

7cb0ab3 · Answer

Minimal development effort can be achieved with BigQuery ML. Also the amount of data is already in BQ.

fitri001 · Answer

Scalability: BigQuery is a serverless data warehouse designed to handle massive datasets. It can efficiently process tens of millions of records daily for model training.
Minimal Development Work: BigQuery ML offers built-in regression models like linear regression that you can train directly on your data stored in BigQuery. This eliminates the need for extensive custom code development with TensorFlow, PyTorch, or scikit-learn (options A, C, and D).
Daily Training Runs:
 BigQuery ML allows scheduling queries for automated model training. You can set up a daily scheduled query to train your model on the latest data.

PST21 · Answer

for scheduling daily training runs with minimal development work and seamless scaling, the best option is to develop a regression model using BigQuery ML (Option B). It allows you to perform model training and inference directly within BigQuery, taking advantage of its distributed processing capabilities to handle large datasets effortlessly.

Carlose2108 · Answer

I went C.

pinimichele01 · Answer

Minimal dev effort => BigQueryML

AzureDP900 · Answer

B. Develop a regression model using BigQuery ML.

You're looking for a solution that scales smoothly and requires minimal development work. BigQuery ML is an excellent choice because it allows you to create machine learning models directly in BigQuery, without the need to write code or set up complex infrastructure.

VinaoSilva · Answer

minimal development work +  regression model = BigQuery ML

Professional Machine Learning Engineer Exam - Question 158

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