314,552 interview questions from 6,000+ companies.
Tests prioritization under pressure, stakeholder management, and ownership when multiple urgent requests compete for limited time.
Tests influence without authority through stakeholder alignment, communication, and ownership in a high-stakes decision.
Tests how you handle ambiguity while maintaining accuracy, documentation discipline, and ownership of the final output.
Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.
Explain how to evaluate a generative model using offline and online methods, with attention to hallucination, product metrics, and experiment design.
Compare RAG and fine-tuning, and decide when each is the better fit for an LLM product.
Design a low latency RAG system over millions of documents, with scalable retrieval, ranking, generation, and production monitoring.
Design a production agent platform that coordinates models, tools, and data sources under strict latency, cost, and safety limits.
Tests understanding of MoE routing, expert activation, and system-level trade-offs.
Tests low-level understanding of attention computations and tensor shape correctness.
Tests ability to reason about performance bottlenecks and memory behavior across LLM lifecycle.
Tests understanding distributed training strategies and scaling trade-offs.
Tests understanding of attention variants and their trade-offs for efficiency and quality.
Tests practical debugging of GPU memory issues and understanding of PyTorch internals.
Tests deriving and implementing core loss computations for training LLMs.
Tests implementing probabilistic decoding strategies for LLM text generation.
Tests understanding of positional encoding mechanisms and their implications.
Tests implementing and analyzing caching for efficient autoregressive generation.
Tests distributed systems reasoning for scaling efficiency and communication bottlenecks.
Tests precise memory accounting for large-model training with mixed precision.
43 total questions
