AIT study guide
AIT · Work through your exam, one topic at a time.
AIT is a professional credential focused on applied artificial intelligence: the vocabulary and concepts behind modern AI systems, the data and machine-learning practices that make them work, and the neural-network, language-model, ethics, and deployment knowledge needed to use them responsibly on the job. It suits analysts, engineers, product and project staff, and technically literate managers who need to reason about AI systems rather than merely operate a tool. The AIT study guide has 6 sections and about 1,821 words.
01Artificial Intelligence Fundamentals and Concepts
Artificial intelligence is the engineering of systems that perform tasks associated with human cognition. Candidates should distinguish narrow (weak) AI , which is task-bound, from general AI , which is hypothetical, and avoid treating today's large models as general intelligence.
329 words, about 2 min02Machine Learning and Data Processing
Machine learning fits models to data to make predictions or decisions. Know the three paradigms: supervised (labeled data; classification and regression), unsupervised (structure finding; clustering, dimensionality reduction), and reinforcement (agent learns from reward signals).
319 words, about 2 min03Neural Networks and Deep Learning
A neural network is layers of neurons (weighted sums plus a nonlinear activation ) trained by backpropagation and gradient descent . Know that nonlinearity is what lets stacked layers learn complex functions; without it, depth collapses to a linear model.
293 words, about 1 min04Natural Language Processing and Generative AI
NLP turns text into representations machines can use. Know tokenization (splitting text into tokens), embeddings (dense vectors where similar meanings are near), and the transformer architecture built on self-attention , which weighs relationships between all tokens.
287 words, about 1 min05AI Ethics, Governance, and Risk
Ethics and governance address how AI is built and used responsibly. Know the recurring principles: fairness , transparency/explainability , accountability , privacy , safety , and human oversight .
288 words, about 1 min06AI Applications, Tools, and Deployment
Deployment turns a model into a reliable service. Know the lifecycle: problem framing, data preparation, training, evaluation, deployment, monitoring , and iteration.
305 words, about 2 min