Free Google Professional Data Engineer Actual Exam Questions - Question 11 Discussion

Maybe A, since linear regression is simple and uses minimal resources, making it ideal for a single limited VM. Neural nets would likely overkill for just predicting prices here.

It’s A. Since it’s a regression problem and we want something lightweight, linear regression fits best. Neural networks (C and D) are too heavy, and logistic (B) is for classification, not prices.

It’s A because linear regression is designed for continuous outcomes like housing prices, and it’s way less demanding on resources than any neural network option here. Neural nets are overkill for this setup.

It’s A. Linear regression is super lightweight compared to neural networks, so it’s the safest bet for a resource-limited VM when predicting prices. Neural nets are just too heavy here.

A/C? Linear regression (A) is straightforward and light on resources, but if the data shows sequential patterns, a simple recurrent neural net (C) might capture that better without too much overhead.

A/B? Since it’s a regression task, B (logistic) is off because it’s for classification. Between A and the neural nets, A is simpler and less resource-heavy, which fits the VM limits better.

Makes sense to go with something lightweight since we’re on a limited VM. I’d rule out C and D because neural networks usually eat up more memory and CPU, which could slow things down a lot. Also, logistic classification (B) is for classification, not regression. So yeah, sticking with linear regression (A) fits the bill for efficiency and purpose here.

This one’s tricky without knowing the dataset size or feature count. But since it’s predicting housing prices (a regression problem), and the VM is limited, linear regression (A) seems the simplest and most resource-friendly. Does anyone know if the question implies a large number of features or any non-linear patterns that would push for neural networks? That might change the choice.