Azure AI Fundamentals (AI-901) cheat sheet

Artificial intelligence is the umbrella discipline; machine learning is a subset that learns patterns from data instead of following hand-written rules.

Supervised learning uses labeled data and solves regression (numeric output) and classification (discrete label) problems.

Unsupervised learning uses unlabeled data; clustering groups similar items, and anomaly detection flags points that deviate from learned patterns.

Reinforcement learning trains an agent by reward and penalty signals across actions, states, and episodes rather than labeled examples.

Computer vision capabilities include image classification, object detection (bounding boxes), semantic segmentation (per-pixel class), and OCR text extraction.

Natural language processing covers text classification, sentiment, named entity recognition, key phrase extraction, translation, and summarization.

Generative AI produces new content (text, images, code) from prompts; large language models are generative models trained on very large text corpora.

Responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Workload-to-capability rule: predicting a numeric value is regression; assigning a category is classification; grouping without labels is clustering.

Microsoft Foundry is the unified platform for building AI apps and agents: a Foundry resource (hub) hosts projects, and each project holds models, data, and connections.

The Foundry portal provides a model catalog, a playground for chat and completions, and evaluation and deployment tooling; the Foundry SDK and REST APIs automate the same operations.

Deployment options differ by hosting: standard (pay-per-token, regional data residency) versus provisioned throughput (reserved capacity, predictable latency) versus serverless API endpoints.

A model deployment exposes an endpoint and credentials; client code calls it with the project endpoint plus a deployment name, not the model family name.

Prompt engineering levers: system message for role and constraints, few-shot examples, explicit output format, and grounding context supplied in the prompt.

Retrieval-augmented generation grounds responses in your own data by retrieving relevant chunks and inserting them into the prompt, reducing unsupported answers.

Azure AI Search can serve as the knowledge source for RAG; Foundry agents can call tools and knowledge sources to complete multi-step tasks.

Evaluation metrics: groundedness, relevance, coherence, and fluency for generative output; precision, recall, and F1 for classification.

Content safety and responsible AI controls (harm categories, severity thresholds, prompt shields) are configured per resource and applied at inference time.