AI0-001 Study Guide — How to Pass CompTIA AI+

Weeks 1–2

AI Fundamentals: machine learning types, neural networks, model lifecycle, AI vs ML vs deep learning vs generative AI

Tip: CompTIA AI+ emphasises breadth over depth. Know the differences: AI is the broad field; ML is learning from data; deep learning is multi-layer neural networks; generative AI creates new content. Know supervised (labelled data, predicts outputs), unsupervised (finds patterns), and reinforcement learning (reward-based).

Weeks 3–4

Generative AI & LLMs: how LLMs work, prompt engineering techniques, tokens, temperature, RAG, hallucination

Tip: Prompt engineering is heavily tested: know zero-shot (no examples), few-shot (a few examples in the prompt), chain-of-thought (step-by-step reasoning), and role prompting (assigning a persona). Know why temperature controls randomness and what top-P/top-K sampling does.

Weeks 5–6

AI in Practice: implementing AI tools, integration patterns, AI APIs, MLOps basics, evaluating AI outputs

Tip: Know how to evaluate AI model outputs: accuracy, precision, recall, F1 score for classification; RMSE/MAE for regression; BLEU/ROUGE for text generation. Know what a confusion matrix shows (true positives, false positives, true negatives, false negatives) and how to interpret it.

Weeks 7–10

Responsible AI & Security: bias, fairness, privacy, explainability, AI regulations, prompt injection, model poisoning, adversarial attacks

Tip: AI security is growing in exam weight. Know AI-specific threats: prompt injection (manipulating model behaviour via crafted inputs), data poisoning (corrupting training data), model inversion (extracting training data from model outputs), adversarial examples (inputs designed to fool models). Know OWASP Top 10 for LLMs.