Which two strategies are best for optimizing resource allocation in deep learning model training? (Select two)

Utilizing multi-GPU parallel processing for training is an effective strategy for optimizing resource allocation in deep learning model training. This approach allows for the distribution of the training workload across multiple GPUs, which can significantly reduce the training time, especially for large models and datasets. By leveraging the computational power of multiple GPUs, you can handle larger batches and scale your training processes horizontally, leading to faster convergence and improved model performance.

Regarding the other options, while network-attached storage can provide access to large datasets, it may introduce latency compared to local storage solutions. Storing datasets on a local SSD with high IOPS can indeed improve data access speeds, which is beneficial but not as impactful as using multiple GPUs for parallel processing. Disabling GPU memory overclocking is generally a stability measure rather than a strategy for optimizing resource allocation, and it does not contribute positively to performance enhancements during training. Therefore, focusing on multi-GPU parallel processing is a clear and effective strategy in the context of optimizing resource allocation in deep learning.