AIAI study guide
AIAI · Work through your exam, one topic at a time.
The Artificial Intelligence Associate (AIAI) is an entry-level credential for people who need to work confidently with AI systems without building them from scratch. It suits business analysts, project and product staff, data-adjacent roles, IT generalists, and career changers who want a shared vocabulary and a practical grasp of how AI projects actually run. The AIAI study guide has 8 sections and about 2,236 words.
01AI Fundamentals and Terminology
Start with the boundaries between AI, machine learning, and deep learning: AI is the broad goal of machine intelligence, ML is the subset where systems learn patterns from data rather than following hand-written rules, and deep learning is ML using multi-layer neural networks. Be able to classify a system correctly (a rules-based chatbot is AI but not ML).
303 words, about 2 min02Machine Learning Concepts and Model Lifecycle
Model work follows a cycle: problem framing, data collection, feature engineering, training, evaluation, tuning, deployment, and monitoring with retraining. Candidates should know what happens at each stage and which artifacts move between them (datasets, feature definitions, model weights, evaluation reports).
295 words, about 1 min03Data Preparation and Data Quality
Data quality is judged on completeness, accuracy, consistency, timeliness, uniqueness, and validity. Candidates should map a symptom to a dimension: duplicate customer records violate uniqueness; a date field with mixed formats violates consistency and validity.
293 words, about 1 min04Generative AI and Large Language Models
Large language models are transformer-based networks trained with self-supervision to predict the next token. Understand tokens (subword units), embeddings, attention, context windows, and autoregressive generation.
261 words, about 1 min05AI Ethics, Bias, and Responsible AI
Bias enters through historical data, sampling, labeling, feature selection, and deployment context. Distinguish representation bias (groups underrepresented in data), measurement bias (a proxy measures the wrong thing), and label bias (annotator or process skew).
268 words, about 1 min06AI Security, Privacy, and Governance
Know classic and AI-specific attacks: data poisoning (corrupting training data), adversarial examples (perturbed inputs causing misclassification), model extraction (stealing behavior via queries), evasion, and membership inference (inferring whether a record was in training data). Generative systems add prompt injection (malicious instructions hidden in content), jailbreaking, and insecure output handling where model output is executed downstream.
263 words, about 1 min07AI Use Cases and Business Applications
Strong candidates translate a business problem into a measurable AI task and state the value hypothesis. Distinguish automation (rules-based, deterministic) from augmentation (AI assists a human) and from autonomous decisioning.
274 words, about 1 min08AI Solution Development and Deployment
Know the components: data pipelines, feature stores, training jobs, model registry, serving layer, and monitoring. Distinguish batch inference (scheduled scoring of large datasets) from real-time inference (low-latency API calls).
279 words, about 1 min