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Google Cloud and TPU AI Infrastructure System Design interview questions

Google Cloud TPU documentation gives a concrete accelerator-system boundary for design practice. Start from the workload and its topology requirements, then separate that design from any claim about Google’s interview prompts. Questions tagged to Google Cloud and TPU appear first, followed by additional practice from our AI Infrastructure System Design track. Company tags indicate preparation relevance, not confirmed interview appearances. Confirm the current interview format with your recruiter.

Design a training service around a topology

  1. Place the job as a unit. Specify the accelerator slice, dataset feed and checkpoint destination. Explain what happens when enough devices exist in total but not in a compatible placement.
  2. Recover consistent progress. Track a durable checkpoint and the input position needed to resume it. Compare waiting for a replacement with restarting on another allocation, including lost work and data access rather than only device count.

Sources: Google Cloud TPU architecture. Reviewed .

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AI Infrastructure System Design questions tagged to Google Cloud and TPU

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Additional AI Infrastructure System Design practice

These questions cover the same topic and are not tagged to Google Cloud and TPU. Use them to practise applying the ideas in a different situation.

15 questions · 10 unlocked for you

Concepts for AI Infrastructure System Design preparation

These concept explanations cover the ideas used in the questions. Check each card for its access level.

Foundational
Inference Platform ArchitectureAn inference platform connects API requests to model replicas. Learn how routing, batching, memory limits and scaling work together to meet a latency target.
Advanced
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Request Routing and Load Balancing for LLMsAn LLM router chooses a compatible replica, weighing queue time against reusable prompt state. Cached work helps only when the extra wait does not erase the saving.
Core
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GPU Job Scheduler DesignA GPU scheduler decides which jobs run, where they run and when resources can be reclaimed. Its placement rules must account for whole jobs, topology and tenant policy.
Advanced
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Training Cluster Design at 10k GPUsSize a training cluster from the model, data and completion deadline. Then check the network, storage, power and recovery capacity needed to keep those GPUs productive.
Advanced
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Serverless GPU PlatformsA serverless GPU platform hides allocation and startup from its users. The design balances cold-start latency, tenant isolation and the cost of idle capacity.
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Multi-Tenant Fine-Tuning ServiceA fine-tuning service turns a tenant’s dataset into an evaluated model or adapter. Its pipeline must protect data while scheduling training and serving the result efficiently.
Advanced
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Evaluation and Data Pipeline InfrastructureData pipelines prepare traceable training inputs; evaluation pipelines compare model behavior. Both need versioned inputs, reproducible execution and useful failure reporting.
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Capacity and BackpressureBackpressure limits accepted work when a service cannot keep up. Bounded queues and clear retry rules prevent overload from spreading through the request path.
GOOGLE CLOUD AND TPU AI INFRASTRUCTURE SYSTEM DESIGN FAQ

This collection includes practice questions such as "Design a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.", "Design serving for a video generation model: diffusion steps, batching, memory, and a latency profile unlike an LLM's.", "Walk me through an inference platform for a hosted LLM. What are the pieces, and what does each one do?". Company tags describe relevance to preparation; they do not confirm that Google Cloud and TPU asked these questions. Check each question for answer access.

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