Fine-tuning a large language model is one of the most valuable skills in AI engineering today — but doing it well takes more than following a tutorial. It requires knowing when to fine-tune at all, how to prepare a genuinely good dataset, which technique actually fits your hardware and goal, and how to evaluate and deploy the result responsibly. This course is built as a rigorous, comprehensive practice-test series designed to test and reinforce your knowledge across the entire fine-tuning lifecycle, from first decision to production deployment.

You'll work through six full practice tests, each covering a distinct area:

Fine-Tuning Fundamentals & Core Concepts — when to fine-tune vs. RAG or prompting, transfer learning, domain adaptation, and base model selection
Data Preparation & Dataset Engineering — dataset formats, sourcing strategies, cleaning, PII removal, and validation
Parameter-Efficient Fine-Tuning Techniques — LoRA, QLoRA, prefix tuning, adapter layers, and hyperparameter tuning
Full Fine-Tuning, RLHF & Alignment Techniques — reward models, preference data, DPO, and constitutional AI
Fine-Tuning Infrastructure & Training Operations — hardware planning, DeepSpeed, monitoring, and experiment tracking
Evaluation, Deployment & Production Best Practices — evaluation methodology, A/B testing, canary rollouts, and ongoing monitoring

Each question includes a detailed explanation connecting the concept to related ideas covered elsewhere in the course, so you're not just memorizing facts — you're building a genuinely connected mental model of how fine-tuning actually works end to end.