240 Questions for AI Systems Deep Dive: LLM & LLMOps Q&A

About this course

Understanding how large language models work at a systems level — not just how to use them — is what separates strong AI engineering candidates from the rest. This course delivers 240 technically rigorous multiple-choice questions built to test and reinforce your understanding of the architecture and operations underlying modern LLMs.

You'll be examined on:

Transformer Internals — Self-attention as a mechanism that maps input sequences to output sequences of the same length using attention heads, transformer block composition (attention + feed-forward layers with residual connections and layer normalization), embedding layers, tokenization, weight tying between the embedding matrix and the language model head, and efficiency techniques like FlashAttention's tiled computation and online softmax.

Training and Generation Mechanics — Self-supervised training via next-word prediction, decoding strategies including greedy decoding, beam search, and temperature-based sampling, plus how conditional generation enables tasks like summarization to be reframed as word-prediction problems using techniques like the tl;dr priming token.

Prompt Engineering at a Technical Level — LLM output configuration (temperature, top-K, top-P), zero-shot and few-shot prompting, structured JSON schema outputs, Chain of Thought, self-consistency, and Tree of Thoughts reasoning.

Context Engineering Fundamentals — The distinction between prompts as instructions and context as everything the model needs to act on them, context package formalism, and system-level context assembly for production applications.

LLMOps Systems Design — API-first deployment, inference latency optimization (kernel fusion, quantization, dynamic batching), capacity management for bursty LLM workloads, LLM tracing, observability metrics (data drift, model safety, model performance), and security practices like LLMSecOps.

Each question is paired

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