Who is this quiz for?

It is aimed at ML engineers, researchers, and serious learners who already know the basics of neural networks and want to test deeper knowledge of transformer and LLM internals. If you are new to the field, start with an introductory explainer first, then return to this quiz.

What topics does the quiz cover?

It covers the core building blocks of modern LLMs: self-attention and multi-head attention, queries-keys-values, positional encoding, layer normalization, residual connections, tokenization, the training objective, the KV cache, and inference-time considerations like context length and sampling.

How is the quiz scored?

Each question is worth one point. After every answer you see whether you were correct and a short explanation, and at the end you get a percentage score plus a list of the topics you missed so you can focus your revision.

Are the explanations accurate enough to learn from?

Yes. Each explanation describes how the mechanism actually works in standard transformer-based LLMs, so the quiz doubles as a quick revision tool. For depth on any single topic, follow the related glossary and explainer pages.

LLM Architecture Quiz: Test Your Deep AI Knowledge

Test your understanding of how LLMs are built

This quiz drills into the architecture that powers modern large language models: the transformer. Below are twenty questions on self-attention, multi-head attention, positional encoding, normalization, tokenization, training objectives, and inference optimization. Each is multiple choice, each is scored, and each comes with an explanation so a wrong answer still teaches the concept.

How it works

Read a question, select the answer you believe is correct, and submit it. The quiz immediately tells you whether you were right and explains the mechanism behind the correct answer. Move through all twenty questions and you will get a final percentage score along with a list of the topics you missed — a ready-made study list for the parts of transformer architecture you should review.

Tips for getting the most out of it

Do not just chase the score. Read every explanation, including for questions you answered correctly, because the reasoning often clarifies a related idea. If you miss several questions on one theme — say attention or the training objective — treat that as a signal to revisit the fundamentals before tackling implementation. Retake the quiz after studying; the goal is durable understanding of how these models actually work, not a one-time number.