Google Professional Data Engineer
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You are tasked with creating a predictive model to estimate housing prices. Given the limited budget, you need to execute this model on a single virtual machine that has constrained computational resources. With these limitations in mind, which learning algorithm would be most appropriate to use?
Exam-Like
A
Linear regression
B
Logistic classification
C
Recurrent neural network
D
Feedforward neural network
Explanation:
Linear regression is the correct answer because it is computationally less resource-intensive compared to other options like neural networks. Given the budget constraints and the need to run the model on a single resource-constrained virtual machine, linear regression is the most efficient choice. It is suitable for predicting continuous numerical values, such as housing prices, and does not require specialized hardware like GPUs, making it a practical solution for this scenario.
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