Databricks Kubernetes, Slurm & GPU Scheduling interview questions

Trace Kubernetes pod placement and Ray bundle reservation as separate layers, including logical GPU limits and autoscaling delay.

Choose the tenant boundary before planning base-model reuse. Account for frozen weights, adapter state and activations separately, then schedule by compatibility, fairness and the real cost of restarting training.

Size notebook capacity from concurrent demand, memory needs and burst tolerance. Choose a sharing mode for the trust model, and make idle release explicit about which state can actually be restored.

Separate quota, borrowing, reclaim latency and fairness, with a demand-conserving example of when pooling improves utilization.

Follow a four-GPU request from advertised capacity to concrete devices, and see why free GPUs across nodes cannot satisfy one pod.

DRA gives the scheduler device claims and attributes to work with. Compare that with extended-resource counts, then check the deployed driver and controller support before promising sharing, partitioning or gang placement.

Diagnose GPU pod failures by scheduling, container startup and execution boundaries, without treating Allocatable as a health certificate.

Separate discovery labels, GPU resource advertisement and validation, and understand the operator as a reconciler with configurable components.

Choose the control plane from job admission, placement, recovery and service needs, with a concrete partial-allocation failure.

Use actual rank placement and measured job duration to decide whether waiting for locality beats starting immediately.

Define warm, cached and truly cold paths, then size transfer budgets and warm capacity without promising universal sub-second restores.

Compare the resource promises of MIG, time-slicing and MPS, then test peak memory and simultaneous demand before packing workloads.

See how partial allocation can strand two training jobs. Separate group admission, real placement, readiness and recovery instead of assuming atomic pod startup.

Design fractional GPU admission around explicit shapes and enforceable guarantees, then reconcile reservations with the runtime.

Make readiness mean that the engine can serve, scale from measured demand and ready capacity, and drain streams before termination. Kubernetes can implement these controls; an operator packages and reconciles them.

Balance durable checkpoint cost against replay and restart loss, with explicit assumptions and a warning sign when the approximation breaks down.

Size usable inference headroom from the complete readiness delay and demand ramp, then decide which training capacity can safely be borrowed.

Build a durable, bounded scheduling loop with reservations and checkpoint-aware preemption, and avoid universal queue-wait formulas.