Gpu Tpu — Google Cloud ML Engineer Practice Questions

GPUs and TPUs are the accelerator hardware options available on Google Cloud for speeding up ML training and inference workloads. The ML Engineer exam distinguishes between GPU-backed compute, which uses NVIDIA hardware and is well-suited for general deep learning frameworks, and Cloud TPUs, which are Google's custom ASICs optimized for large-scale TensorFlow and JAX workloads. Candidates must understand how to select the appropriate accelerator type and count for a given workload, how to attach accelerators in Vertex AI training jobs, and the cost tradeoffs between accelerator configurations. TPU Pod configurations and their use in large language model training are also within scope.

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