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Generative AI Leader Practice Question: A data scientist wants to run large-scale…
A data scientist wants to run large-scale distributed training of a custom deep learning model using Google's custom AI accelerators. Which infrastructure should they choose to minimize cost while leveraging Google's proprietary chips?
⚠ Common exam trap
Candidates often confuse 'custom AI accelerators' with any high-performance hardware like GPUs, but the question specifically requires Google's proprietary chips (TPUs), and Cloud TPU v5e is the only option that directly provides cost-optimized, large-scale distributed training using Google's own accelerators.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cloud TPU v5e
Cloud TPU v5e is the correct choice because it is Google's proprietary custom AI accelerator designed specifically for large-scale distributed training of deep learning models, offering superior cost-efficiency compared to GPUs for many workloads. TPU v5e provides a balanced price-performance ratio for medium-to-large training tasks, and Google's TPU architecture is optimized for TensorFlow and JAX, enabling efficient scaling across multiple TPU pods. This minimizes cost while leveraging Google's custom chips, as opposed to using NVIDIA GPUs which are not Google's proprietary hardware.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud TPU v5e
Why this is correct
Cloud TPU v5e is Google's cost-optimised Tensor Processing Unit generation, purpose-built for large-scale distributed training on Google's proprietary accelerators. It satisfies the stem's dual constraint of minimising cost while leveraging Google-designed chips, unlike GPU-based or general-purpose compute options.
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Compute Engine with NVIDIA A100 GPUs
Why it's wrong here
NVIDIA A100 GPUs are not Google's proprietary accelerators, so this fails the TPU requirement and typically costs more per chip-hour. It is tempting because Compute Engine offers flexible, high-performance GPU instances, and would be correct when training frameworks depend on CUDA or NVIDIA-specific libraries rather than TPUs.
- ✗
Vertex AI Workbench with custom machines
Why it's wrong here
Vertex AI Workbench with custom machines provisions general-purpose CPU or GPU instances, not Google's proprietary TPU accelerators. It is tempting because Workbench integrates with Vertex AI training pipelines, and would be correct when the workload needs managed notebook environments on standard compute rather than TPU-specific hardware.
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Google Colab Pro with TPU runtime
Why it's wrong here
Colab Pro provides interactive notebook sessions with limited TPU quotas and no support for sustained large-scale distributed training. It is tempting because it does expose genuine TPU runtimes cheaply, and would be the right choice for prototyping or small experiments rather than production-scale distributed training.
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