Google Professional Machine Learning Engineer Question 279
Single answerGoogle Cloud PlatformYou are training a deep learning model on Google Cloud that involves processing a large dataset and requires extensive matrix computations. The training process is taking longer than expected due to the high computational demands. Which hardware option should you choose to significantly accelerate the training process?
- A
Use TPU (Tensor Processing Unit) for its specialized hardware acceleration of machine learning workloads.
- B
Switch to standard n1-standard-4 CPU instances to reduce costs.
- C
Increase the disk storage capacity to handle larger datasets.
- D
Use GPU (Graphics Processing Unit) instances to leverage parallel processing for faster computations.
Show answer and explanation
Correct answer: A
Explanation
When training deep learning models with high computational demands, choosing the right hardware is critical. TPUs are optimized for machine learning workloads, offering significant speed improvements over general-purpose CPUs or GPUs. While GPUs are commonly used for accelerating computations, TPUs are specifically designed for TensorFlow and other machine learning frameworks, making them the preferred option in this scenario.
- A. Correct.
TPUs (Tensor Processing Units) are specifically designed for accelerating machine learning workloads, especially deep learning models, making them the best choice for this scenario.
- B. Incorrect.
While n1-standard-4 CPU instances are cost-effective, they are not optimized for handling computationally intensive tasks like deep learning model training.
- C. Incorrect.
Increasing disk storage capacity is useful for managing larger datasets but does not directly address the problem of slow training due to high computational demands.
- D. Incorrect.
GPUs are effective for parallel processing and accelerating computations, but TPUs are generally more efficient for deep learning tasks on Google Cloud.