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Databricks AI Infrastructure System Design interview questions

Practise ai infrastructure system design for Databricks ML Platform Engineer roles. Design an inference platform at 10k requests per second, a 10,000-GPU training cluster, a job scheduler with preemption and checkpointing, a serverless GPU runtime with sub-second cold starts, a multi-tenant fine-tuning service, an eval pipeline. The whiteboard round at OpenAI, Anthropic, Baseten and Together. Questions tagged to Databricks 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.

PREPARATION CONTEXT · Model serving, fine-tuning and vector search on the platform. Sources and confidence in the Databricks guide →

AI Infrastructure System Design questions tagged to Databricks

4 questions · 1 unlocked for you

Additional AI Infrastructure System Design practice

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

15 questions · 9 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.
Advanced
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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.
Advanced
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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.
DATABRICKS AI INFRASTRUCTURE SYSTEM DESIGN FAQ

This collection includes practice questions such as "Design the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.", "Design serving for 100 fine-tuned variants of one 70B base. Multi-LoRA on shared replicas or a replica per variant?", "Design the registry and deploy pipeline for model weights: 141 GB artifacts, versioning, distribution to a thousand nodes, integrity.". Company tags describe relevance to preparation; they do not confirm that Databricks asked these questions. Check each question for answer access.

Other topics for Databricks preparation

Explore the other topics mapped to Databricks in our preparation guide.

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