Google Professional Machine Learning Engineer Question 334
Single answerGoogle Cloud PlatformYou are tasked with building a machine learning model to process large batches of images for training a computer vision classification model. The model has a complex architecture involving convolutional neural networks (CNNs) with millions of parameters. Training must be completed as quickly as possible, and cost efficiency is a secondary concern. Which hardware option should you choose to optimize training performance?
- A
Standard CPUs
- B
GPUs
- C
TPUs
- D
Edge devices
Show answer and explanation
Correct answer: C
Explanation
In this scenario, the training of a complex CNN model with millions of parameters requires hardware optimized for deep learning tasks and large-scale matrix computations. TPUs are purpose-built for such workloads and provide significantly faster training performance compared to GPUs or CPUs. While GPUs are also a strong contender, TPUs excel in this specific scenario where speed is the primary concern. CPUs and edge devices are not suitable for this task due to their limited computational power and lack of parallel processing capabilities.
- A. Incorrect.
Standard CPUs are not well-suited for computationally intensive tasks like training deep learning models with CNNs. They lack the parallel processing capabilities needed for efficient matrix operations, making them a poor choice for this scenario.
- B. Incorrect.
GPUs are a good option for training deep learning models as they provide excellent parallel processing capabilities. However, for models with extremely high computational demands and a need for maximum speed, TPUs are often more optimal.
- C. Correct.
TPUs are specifically designed for deep learning workloads, especially for large-scale matrix operations as involved in CNNs. They provide significantly faster training times than GPUs or CPUs, making them the best choice for this scenario where speed is the top priority.
- D. Incorrect.
Edge devices are designed for low-power, low-latency inference tasks and are not suitable for training large and complex models like CNNs. They lack the computational power required for this use case.