Large Language Models (LLM) Concepts Practice Tests

About this course

This practice test series is designed to build a genuine, working understanding of how a large language model actually functions — not just surface-level familiarity, but the mechanics underneath the tools you use every day.

Across 6 tests and 600 carefully written multiple-choice questions, you'll work through:

Foundations of Language Models covers what an LLM is, how it tokenizes and represents text, next-token prediction, embeddings, context windows, and sampling parameters like temperature.

Transformer Architecture covers self-attention, multi-head attention, positional encoding, the feed-forward layer, residual connections, and the encoder/decoder distinction underlying a modern model.

Training & Fine-Tuning Concepts covers pretraining, fine-tuning, instruction tuning, RLHF, overfitting, hyperparameters, catastrophic forgetting, and parameter-efficient methods.

Prompting & In-Context Learning covers zero-shot and few-shot prompting, chain-of-thought, role prompting, context management, RAG, and prompt injection awareness.

Model Behavior & Limitations covers hallucination, bias, knowledge cutoffs, sycophancy, poor calibration, jailbreaking, and over-refusal.

Applications & Ecosystem covers function calling, vector databases, agentic workflows, choosing between fine-tuning/RAG/prompting, deployment tradeoffs, and application-level guardrails.

Every question — right and wrong answers alike — comes with its own explanation, so you understand the reasoning behind each option, not just which one is correct. This course is built for a developer, engineer, or technically curious learner who wants a durable, transferable understanding of LLMs that will remain useful even as the specific tools continue to evolve.

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