OCI AI Foundations study guide
OCI AI Foundations · Work through your exam, one topic at a time.
OCI AI Foundations is an entry-level certification from Oracle that validates whether you can talk sensibly about artificial intelligence and machine learning in an Oracle Cloud Infrastructure context. It is aimed at people who are new to AI rather than specialists: developers and data analysts moving into AI work, solution architects and sales or pre-sales engineers who need to position OCI AI services, and business-facing staff who have to reason about what a model can and cannot do. The OCI AI Foundations study guide has 6 sections and about 2,703 words.
01AI and Machine Learning Fundamentals
AI is the broad discipline; machine learning (ML) is the subset where models learn patterns from data instead of being explicitly programmed; deep learning (DL) is the subset of ML built on multi-layer neural networks. A scenario describing hand-written rules by a developer is not ML.
346 words, about 2 min02Deep Learning and Neural Networks
A neuron computes a weighted sum of inputs plus a bias, then applies a nonlinear activation. Stacked layers form a network: input layer, hidden layers, output layer.
356 words, about 2 min03Generative AI, Large Language Models, and Prompt Engineering
Discriminative models learn P(y x) - they label. Generative models learn the data distribution so they can create new samples (text, images, audio, code).
1,026 words, about 5 min04OCI AI Services
OCI AI Services are pretrained, API-driven models - you call them without training a neural network yourself. Map the input to the service: Text → OCI Language: sentiment, key phrases, entities, language detection, translation, text classification (including custom models), and PII detection/masking.
315 words, about 2 min05OCI Data Science and AI Infrastructure
A managed workspace for the ML lifecycle: projects, notebook sessions running in conda environments, jobs for scheduled or long-running batch work, the model catalog for versioned artifacts, model deployments that expose an HTTP endpoint with autoscaling, and ML pipelines for orchestration. Notebook sessions are interactive and can be idle-stopped; jobs are non-interactive and terminate when finished.
318 words, about 2 min06Responsible AI, Ethics, and Security
Responsible AI covers fairness, transparency and explainability, privacy, accountability, safety and human oversight. Bias enters through data, labels and design; you can only measure it if you capture the relevant group attributes.
342 words, about 2 min