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Serverless Architecture and Functions as a Service Questions

Covers architectural and operational aspects of serverless computing and Functions as a Service platforms such as Lambda, Cloud Functions, and Azure Functions. Topics include event driven design patterns, stateless versus stateful approaches, state management strategies, serverless friendly data stores, function lifecycle and cold start behavior, concurrency and scaling characteristics, cost and billing models, vendor lock in and portability concerns, integration with messaging and event buses, security and permission models, observability and monitoring for ephemeral compute, and decision criteria for choosing serverless versus traditional compute or container based deployments. Candidates should be able to reason about trade offs, design event driven backends, and propose operational practices to manage reliability and performance in serverless environments.

MediumSystem Design
38 practiced
How would you implement canary or A/B testing for two serverless ML model versions (v1 and v2) with minimal customer impact? Describe traffic splitting, instrumentation to collect model-level metrics, statistical significance criteria for rollouts, and automated rollback triggers. Mention platform features (API Gateway weighted routing, Feature Flags) and data collection best practices.
HardSystem Design
41 practiced
You run a critical ML inference endpoint with unpredictable traffic bursts and a p99 latency requirement of <= 75ms. Provisioned concurrency is expensive. Propose a hybrid architecture minimizing cost while meeting latency SLOs across bursts: include a warm-pool strategy, autoscaling containers/FaaS mix, caching layers, and degraded-mode fast models. Explain autoscaling behavior and when to fall back to degraded responses.
MediumTechnical
40 practiced
Write Python pseudo-code for a decorator that first checks an in-memory LRU cache for a feature vector, falls back to DynamoDB when missing, and spawns an asynchronous refresh to populate the cache for future calls. In your design explain thread-safety considerations, memory limits in serverless runtimes, and how to avoid stampeding cache fills under concurrent cold starts.
EasyTechnical
35 practiced
How do common FaaS platforms scale when traffic increases rapidly? Use AWS Lambda or an equivalent platform as an example to explain how new instances (execution environments) are provisioned, per-account or region concurrency limits, reserved or provisioned concurrency, and the implications for an ML inference endpoint under bursty traffic.
HardTechnical
38 practiced
Design a CI/CD pipeline for serverless ML model deployments that includes unit tests, integration tests, model validation (accuracy/regression tests), artifact signing, canary deployments, and rollback. Explain how to integrate cold-start and scaling tests into CI, and how to automate promotion of model artifacts across staging to production while preserving auditability.

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