Google Professional Machine Learning Engineer
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Ultimate access to all questions.
As a data scientist at an industrial equipment manufacturing company, you're tasked with developing a regression model to predict power consumption in manufacturing plants based on daily sensor data. With tens of millions of records collected each day, you need a solution that scales efficiently, requires minimal development effort for daily training runs, and adheres to the company's strict data privacy and compliance standards. Given these constraints, which of the following approaches is the BEST to meet these requirements? Choose one correct option.
A
Build a regression model using BigQuery ML, leveraging its built-in data encryption and access control features to comply with data privacy standards.
B
Train a regression model using AutoML Tables, utilizing its automated data preprocessing and model tuning capabilities to minimize development effort and ensure scalability.
C
Create a custom TensorFlow regression model, and optimize it with Vertex AI Training, taking advantage of Vertex AI's managed infrastructure for scalability and compliance.
D
Construct a custom scikit-learn regression model, and refine it with Vertex AI Training, using Vertex AI's features for automated scaling and compliance checks.