Google Cloud GenAI Offerings (35%)
Hardware & Compute
KEY CONCEPTS
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WHAT THE EXAM IS REALLY TESTING
Understand the differences and use cases for each hardware type. Exam tests when to use TPUs, GPUs, or CPUs for different ML workloads.
COMMON TRAPS
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OFFICIAL DOCUMENTATION
STUDY Q&A
- What are TPUs and what workloads are they optimized for?TPUs (Tensor Processing Units) are custom accelerators developed by Google for high-performance machine learning workloads, especially deep learning and large-scale training and inference. They are optimized for tensor operations and are widely used for training and serving large models.
- What are iGPUs and how do they differ from dedicated GPUs?iGPUs (integrated GPUs) are graphics processors built into the CPU, sharing memory with the main processor. Dedicated GPUs are separate hardware components with their own memory, offering significantly higher performance for AI and ML workloads.
- Why are GPUs fundamental for training AI models?GPUs (Graphics Processing Units) are fundamental for AI model training because they can perform parallel computations on large datasets, accelerating the training of deep learning models compared to CPUs.
- What is CUDA and what role does it play in using GPUs?CUDA is a parallel computing platform and API developed by NVIDIA that allows developers to use NVIDIA GPUs for general purpose processing, including machine learning and deep learning workloads.