System Design in Coding Questions

Assess the ability to apply system design thinking while solving coding problems. Candidates should demonstrate how implementation level choices relate to overall architecture and production concerns. This includes designing lightweight data pipelines or data models as part of a coding solution, reasoning about algorithmic complexity, throughput, and memory use at scale, and explaining trade offs between different algorithms and data structures. Candidates should discuss bottlenecks and pragmatic mitigations such as caching strategies, database selection and schema design, indexing, partitioning, and asynchronous processing, and explain how components integrate into larger systems. They should be able to describe how they would implement parts of a design, justify code level trade offs, and consider deployment, monitoring, and reliability implications. Demonstrating this mindset shows the candidate is thinking beyond a single function and can balance correctness, performance, maintainability, and operational considerations.

MediumSystem Design

0 practiced

You're asked to decompose an ML pipeline into microservices: feature extraction, model scoring, and post-processing. Propose service boundaries, the communication patterns (sync/async), data contracts between services, and how you'd enable fault isolation and independent scaling for each component.

EasyTechnical

0 practiced

Describe what a feature store is and outline a simple API for online feature retrieval used during inference (input: entity_id, feature_names, request_timestamp). Discuss the latency and consistency constraints for the API, and how you'd handle late-arriving feature updates or feature backfilling in production.

MediumSystem Design

0 practiced

Design a lightweight feature validation service that runs at ingestion time to reject or tag suspicious feature vectors (e.g., out-of-range values, schema mismatches, or correlated anomalies). Describe the checks, how to maintain low latency (<5ms) per ingestion, and how to surface rejections to downstream pipelines and operators.

MediumSystem Design

0 practiced

Multiple teams will deploy models onto a shared cluster. Design resource allocation and multi-tenancy strategy: how to handle GPU allocation, autoscaling policies, quota enforcement, tenant isolation, and cost tracking to prevent noisy neighbors impacting other teams.

MediumTechnical

0 practiced

Implement a thread-safe in-memory LRU cache in Python that supports get(key), put(key, value, ttl_seconds=None), and evicts least-recently-used entries when capacity is reached. Also ensure TTL-based expiration. Provide complexity guarantees and describe how you'd test it under concurrency.

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