hardMultiple Choice
Generative AI Leader Practice Question: A data science team wants to run a large-scale…
A data science team wants to run a large-scale transformer training job with custom model architectures. They need the highest compute density for a multi-node job and want to minimize inter-node communication latency. Which Google Cloud infrastructure is BEST suited for this workload?
⚠ Common exam trap
It's easy for candidates to assume GPU clusters with standard networking (Option B) are sufficient for multi-node training, underestimating how drastically inter-node latency impacts scaling efficiency for transformer models with large parameter counts.
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
✓
TPU v4 Pod slices with high-speed inter-chip interconnect
TPU v4 Pod slices provide the highest compute density for multi-node transformer training by using a custom 3D torus interconnect with 800 Gbps per chip bandwidth, which minimizes inter-node communication latency far below what standard networking can achieve. This architecture is specifically designed for large-scale model parallelism, making it ideal for custom transformer architectures that require frequent all-reduce and collective communication operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A single TPU v5e VM with multiple accelerators
Why it's wrong here
A single TPU v5e VM with multiple accelerators cannot scale across nodes, so it fails the multi-node requirement for highest compute density. It is correct for smaller single-host training or inference workloads that fit within one VM.
- ✗
A cluster of A100 GPU VMs connected via standard networking
Why it's wrong here
A100 VMs over standard networking lack the dedicated high-bandwidth interconnect that multi-node transformer training demands, so inter-node communication latency stays high. It is tempting because A100 clusters genuinely suit single-node or loosely coupled GPU workloads, where that interconnect is unnecessary. The scenario's explicit latency-minimisation requirement rules it out.
- ✓
TPU v4 Pod slices with high-speed inter-chip interconnect
Why this is correct
TPU v4 Pod slices link thousands of chips through a dedicated high-speed inter-chip interconnect, delivering the densest multi-node compute with minimal inter-node latency. This directly satisfies the stem's demand for maximum compute density and reduced communication overhead during large-scale transformer training.
- ✗
Cloud Run jobs with GPU acceleration
Why it's wrong here
Cloud Run jobs with GPU acceleration run containerised batch tasks and provide no multi-node interconnect fabric, so inter-node latency cannot be minimised. They are correct for short-lived, event-driven or batch container workloads rather than tightly coupled distributed training.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.