Lambda Kubernetes, Slurm & GPU Scheduling interview questions

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

Compare the cost of committed capacity with the demand each additional GPU would actually serve. Keep billing discounts separate from capacity guarantees, and account for bursts without counting the same work twice.

Use a short admission check for recent changes and a deeper acceptance suite for new or repaired nodes. Set thresholds from matched healthy baselines, and make quarantine and return-to-service decisions explicit.

Follow a GPU request from node inventory through allocation, device confinement and task launch. A single-node, eight-task example shows how resource shape and per-task device numbering fit together.

Treat the upgrade as a tested change to a compatibility set. Drain safely, canary representative workloads, size waves from usable spare capacity and rehearse restoration of the previous working configuration.

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.

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.

Calculate traffic across the actual inter-cloud boundary before deciding. Whole-job placement often avoids repeated transfers, but communication volume, overlap, convergence requirements and available links determine whether a split job is viable.