Google Professional Machine Learning Engineer Question 282
Single answerGoogle Cloud PlatformYou are training a deep learning model for image classification on a large dataset consisting of millions of high-resolution images. The training process involves computationally intensive operations such as convolution and backpropagation. Which hardware configuration would be the most appropriate for this workload?
- A
Standard CPUs with high memory capacity
- B
GPUs with a high number of CUDA cores
- C
TPUs optimized for matrix multiplication
- D
Custom ASICs designed for specialized tasks
Show answer and explanation
Correct answer: B
Explanation
GPUs are the most appropriate choice for training deep learning models with large datasets due to their ability to perform parallelized computations efficiently. They are specifically designed for workloads requiring operations like matrix multiplication and convolution, making them the ideal hardware choice in this scenario. While TPUs are also highly efficient, they are more specialized for TensorFlow workloads and may not provide the same versatility as GPUs for general-purpose deep learning tasks.
- A. Incorrect.
Standard CPUs with high memory capacity are not well-suited for parallelized operations like those involved in deep learning training, as they lack the specialized architecture for such tasks.
- B. Correct.
GPUs with a high number of CUDA cores are optimized for parallel processing, making them highly suitable for computationally intensive tasks like deep learning training, especially when dealing with large datasets and convolutional operations.
- C. Incorrect.
TPUs are indeed optimized for matrix multiplication and are efficient for large-scale training, but they are more suitable for specific types of workloads like TensorFlow models, and GPUs are generally more versatile for this scenario.
- D. Incorrect.
Custom ASICs are designed for highly specialized tasks, and while they can be efficient, they are not a general-purpose solution for deep learning training and lack the flexibility provided by GPUs or TPUs.