Domain and Product Technical Knowledge Questions

Evaluation of deep, domain specific technical knowledge relevant to the team, product, or role. Candidates should demonstrate subject matter expertise in the relevant problem space and be able to explain core concepts, architectures, algorithms, and practical engineering trade offs. Example domains include recommendation systems, data platform engineering, security, and analytics, as well as platform areas such as application programming interface platform management, developer experience, deployment orchestration, infrastructure and reliability, and observability. Expect questions on domain specific algorithms, data pipelines, real time versus batch trade offs, feature stores, data governance, versioning strategies, integration patterns, common customer use cases, and typical product pain points. For product focused roles, be prepared to explain core product features, typical customer workflows, integration points, and how domain constraints influence product decisions. For role or platform focused discussions, describe how the domain shapes responsibilities, challenges, and priorities and outline approaches to initial discovery, diagnosis, and early improvements. This topic tests both conceptual depth and the ability to map domain knowledge to concrete product and engineering decisions.

MediumSystem Design

61 practiced

Design API contracts and access controls for an internal model-as-a-service platform used by multiple internal teams. Cover authentication/authorization, per-tenant rate-limiting, model version routing, SLA tiers, billing/quotas, multi-tenant isolation, and observability endpoints you would expose to consumers.

HardSystem Design

62 practiced

Design an end-to-end architecture to serve personalized recommendations for 100M users with sub-100ms latency and daily model updates. Cover streaming and batch data ingestion, distributed training, embedding storage and retrieval, caching hierarchy, feature freshness strategy, deployment patterns, and SLOs to monitor.

MediumTechnical

68 practiced

Explain strategies to version and manage prompts and instruction sets used in LLM-driven product features. Cover storage/version control, automated test suites, A/B testing of prompt variants, rollout and rollback, auditability, and handling non-deterministic outputs for reproducibility.

MediumSystem Design

52 practiced

Design a Retrieval-Augmented Generation (RAG) system for a customer support product. Specify index strategy, embedding store design, retrieval algorithm and re-ranker, prompt assembly, LLM orchestration and cost/latency trade-offs, freshness/updating of the index, and content-moderation strategies.

MediumSystem Design

49 practiced

Design a feature pipeline for a recommender product that needs both offline aggregate features (e.g., 7-day click rates) and online session-level features (e.g., last 5 interactions). Describe event ingestion, transformation steps, freshness requirements, storage choices (stream vs batch), and approaches to maintain training-serving consistency.

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