Advance LLMs, AI Agents, MCP & RL - Interview Q&A / Tests

About this course

Our expertly crafted practice tests are aligned with the most recent breakthroughs in AI, ensuring both comprehensive coverage and focused depth on high-impact areas. These tests are tailored to assess your understanding across a wide spectrum of cutting-edge topics including:· LLM Pre-Training: Data curation strategies, scaling laws, decoding techniques, KV caching, learning rate schedulers, adapters, and fully sharded data parallelism (FSDP).· LLM Architectures & Optimization: Advanced components like Flash Attention, Mixture of Experts (MoE), Switch Transformers, and evaluation tools such as LLM Judges and Self-Instruct frameworks.· Fine-Tuning & Alignment: Techniques such as GRPO, PPO, DPO, DAPO, REINFORCE with Leave-One-Out (RLOO), RLHF, and ReAct-style tool-augmented finetuning.· Specialized Frameworks: LangGraph for agentic AI workflows, Model Context Protocol (MCP) for dynamic tool integration, and DeepSeek models with multi-stage training pipelines including reinforcement learning and distillation.· Model Compression & Inference: Knowledge distillation, quantization (including advanced methods like 2-bit packing), and test-time scaling for cost-efficient deployment.· Retrieval-Augmented Generation (RAG): Deep understanding of the RAG triad—context relevance, groundedness, and answer relevance—and their role in evaluating truthful and context-aware responses.· LLM Evaluation & Tooling: Techniques for systematic evaluation of model outputs, reward modeling, rejection sampling, and human-in-the-loop strategies.· Infrastructure & Deployment: Insight into modern GPU architectures and performance optimization for pretraining and inference workloads.

Additionally, the course includes real-world interview questions from top-tier tech companies, making it ideal for aspiring AI engineers aiming to

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