Scalability and Systems Resource Management Questions
Design and operational practices for managing compute and platform resources as systems scale. Covers autoscaling, resource pooling, orchestration, cost trade offs between always on versus on demand provisioning, and architectural choices that affect resource utilization and performance. Candidates should be prepared to discuss capacity planning for infrastructure, metrics and alerts for autoscaling, and cost versus performance decisions for high availability systems.
EasyTechnical
0 practiced
Describe the differences between layer 4 (transport) load balancing and layer 7 (application) load balancing. For each type, explain one scaling implication (e.g., TLS termination, sticky sessions, routing) and how that affects autoscaling decisions and resource utilization.
HardTechnical
0 practiced
For a cluster that runs many bursty CPU-bound jobs, propose a CPU allocation strategy that maximizes utilization but prevents SLO violations for latency-sensitive services. Explain tradeoffs between CPU shares, CFS quotas, burstable QoS, and CPU pinning for critical services.
HardTechnical
0 practiced
Design a placement algorithm to schedule pods with constraints: GPU requirement, node-affinity, taints/tolerations, and a need to maximize utilization and fairness. Describe data structures, scoring function, and how you would support scale to thousands of pods per second in a scheduler.
MediumTechnical
0 practiced
Explain how caching strategies (in-memory LRU, distributed cache, CDN) reduce compute resource needs. For a highly dynamic dataset with 1-minute TTL requirements, recommend a caching strategy and TTL tuning approach that balances hit rate and staleness.
HardTechnical
0 practiced
You are asked to design Terraform modules and a CI pipeline to provision autoscaling groups (or managed instance groups) across environments. Describe module interfaces, state management, promotion workflow, and how to test autoscaling behavior safely as part of the pipeline.
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