面试题库

全部 Django Elasticsearch FastAPI Flask Function Call JVM Java IO Java 后端 Java 基础 Java 并发 Java 新特性 Java 集合 JavaWeb Kafka LLM LangChain LangGraph MongoDB MyBatis MySQL Netty Nginx Python RAG RabbitMQ Redis RocketMQ Spring Spring AI SpringBoot SpringCloud SpringMVC ZooKeeper 分布式 后端技术 大厂面经 大数据 微服务 提示词工程 操作系统 数据库 数据结构 智能体 海康威视2026秋季社招专项 消息队列 监控运维 算法 系统设计 缓存 计算机基础 计算机网络 设计模式 面试题 高可用 高并发
LLM 中等

Explain how masking works in masked self-attention in Transformer

作答 →
LLM 中等

What is the significance of self-supervised learning in LLM pretraining?

作答 →
LLM 中等

What are the various bottlenecks in a typical LLM inference pipeline when running on a modern GPU?

作答 →
LLM 中等

Explain the throughput vs. latency tradeoff in LLM inference

作答 →
LLM 中等

Differentiate between online and offline LLM inference deployment scenarios and discuss their respective requirements

作答 →
LLM 中等

What is a system prompt, and how does it differ from a user prompt?

作答 →
LLM 中等

Why is context length important when designing prompts for LLMs?

作答 →
LLM 中等

What are the different approaches for choosing examples for few-shot prompting?

作答 →
LLM 中等

Why is context important in designing prompts?

作答 →
LLM 中等

What is self-consistency prompting, and how does it improve reasoning?

作答 →
LLM 中等

What is In-Context Learning (ICL), and how is few-shot prompting related?

作答 →
LLM 中等

How would you design a scalable LLM inference system for real-time applications?

作答 →