AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

A key advantage of using a GPU in training models is its parallel processing capabilities. Unlike traditional CPUs, which typically have a limited number of cores optimized for sequential processing tasks, GPUs are designed with thousands of smaller cores that can handle numerous operations simultaneously. This architecture makes GPUs exceptionally well-suited for tasks that involve large-scale matrix and tensor calculations, which are prevalent in machine learning and deep learning models.

When training these models, especially neural networks, the computations required can be very intensive and involve processing large datasets. The parallel nature of GPUs allows for these calculations to be distributed across many cores at once, drastically reducing the time needed to train a model compared to using a CPU that processes tasks sequentially. As a result, tasks such as forward and backward propagation in neural networks can be performed much more efficiently, leading to faster convergence and model development.

The other options, while they touch on aspects that may be relevant in different contexts, do not capture the fundamental reason why GPUs are favored in training machine learning models. Increased memory capacity is not specifically associated with GPUs, as both GPUs and CPUs can have varying memory configurations. Lower energy consumption is generally not a distinguishing feature of GPUs compared to CPUs; in fact, GPUs can consume a significant amount of power depending on