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Oracle Cloud Infrastructure AI Foundations Associate Fact Sheet

Exam Overview

Exam Code: 1Z0-1122-25 Exam Name: Oracle Cloud Infrastructure AI Foundations Associate Level: Foundational Duration: 60 minutes Format: Multiple choice Questions: 40 Passing Score: 65% Cost: USD 245 list price; Oracle frequently runs free certification periods for its AI and OCI foundations exams Valid For: Oracle refreshes these annually with a year suffix in the exam code Delivery: Oracle CertView, online proctored Prerequisites: None

Verify before booking. Oracle re-versions these exams every year (the -25 suffix), and free certification promotions come and go. Confirm the current exam code, price, and whether a free window is open before you register.

📖 Oracle Cloud Infrastructure AI Foundations Associate - exam page and preparation track 📖 Oracle University free AI learning paths - the official free training 📖 OCI AI services documentation - product reference

Why this exam is in this repo

Oracle has five OCI certifications in this repo and, until now, zero AI ones, in a repository whose remit is explicitly cloud plus AI. That is the gap this closes.

It also has a practical draw: Oracle regularly makes this exam and its training free, which makes it one of the cheapest ways to get a vendor AI credential.

Target Audience

  • Anyone new to AI who wants a structured, vendor-anchored overview
  • OCI practitioners adding AI vocabulary
  • Sales, product, and management roles needing credible AI literacy
  • A stepping stone to OCI Generative AI Professional

No coding is required, and no prior AI knowledge is assumed.

Exam Domains

Oracle publishes this as a topic list rather than weighted domains.

AI fundamentals

Key Concepts: - What AI is, and how AI, machine learning, and deep learning relate - AI task categories: language, speech, vision, and decision - Common applications and their business value - Responsible AI: fairness, transparency, explainability, accountability, and privacy

Machine learning fundamentals

Key Concepts: - Supervised learning: classification and regression - Unsupervised learning: clustering, dimensionality reduction, anomaly detection - Reinforcement learning at a conceptual level - The ML workflow: data collection, preparation, feature engineering, training, evaluation, deployment, monitoring - Training, validation, and test splits - Overfitting, underfitting, and the bias-variance trade-off - Evaluation metrics: accuracy, precision, recall, F1, confusion matrix, and why accuracy alone misleads on imbalanced data - Common algorithms: linear and logistic regression, decision trees, random forests, k-means, support vector machines

Deep learning fundamentals

Key Concepts: - Neural network structure: neurons, layers, weights, biases, activation functions - Forward propagation, loss functions, backpropagation, gradient descent - Hyperparameters: learning rate, epochs, batch size - Architectures: CNN for images, RNN and LSTM for sequences, transformers for language - Why GPUs matter for training, and the role of parallelism

Generative AI and large language models

Key Concepts: - Generative versus discriminative models - Transformer architecture, attention, tokens, and embeddings - Pre-training, fine-tuning, and instruction tuning - Prompt engineering: zero-shot, few-shot, chain-of-thought - Context window and its limits - Hallucination and how grounding mitigates it - Retrieval-augmented generation (RAG) - Vector databases and semantic search - Agents and tool use at a conceptual level

OCI AI services

Key Concepts: - OCI Generative AI - managed access to foundation models, with dedicated AI clusters and custom model fine-tuning - OCI Generative AI Agents - retrieval-augmented agents over enterprise data - OCI Language - sentiment, entity recognition, key phrase extraction, translation, PII detection - OCI Speech - speech to text transcription - OCI Vision - image classification, object detection, document AI - OCI Document Understanding - extraction from forms and documents - OCI Data Science - notebooks, model catalog, model deployment, jobs, pipelines - OCI Data Labeling - Select AI and AI Vector Search in Autonomous Database

OCI AI infrastructure

Key Concepts: - GPU shapes and bare metal compute for AI workloads - RDMA cluster networking for distributed training - Storage options for training data - Where each service sits: infrastructure, platform, or ready-made service

The service selection table

The most testable content on the exam.

Requirement Service
Analyze sentiment in customer reviews OCI Language
Transcribe recorded calls OCI Speech
Detect objects in photographs OCI Vision
Extract fields from scanned invoices OCI Document Understanding
Build and train a custom model in a notebook OCI Data Science
Call a foundation model through an API OCI Generative AI
Answer questions grounded in internal documents OCI Generative AI Agents
Run distributed training across many GPUs OCI AI infrastructure with RDMA cluster networking
Query a database with natural language Select AI in Autonomous Database