OCI Generative AI Professional (1Z0-1127-25) Study Guide

Domain 1: Fundamentals of Large Language Models

Key concepts you must know · 168 practice questions

LLM architectures: decoder-only (GPT-style, autoregressive), encoder-only, and encoder-decoder models.
Transformer fundamentals: self-attention, multi-head attention, positional encoding, and context windows.
Tokenization, tokens versus words, and how token limits affect cost and context.
Decoding and sampling: greedy decoding, beam search, and the effect of temperature, top-k, and top-p on output.
Prompt engineering: zero-shot, few-shot in-context learning, chain-of-thought, and system versus user prompts.
Fine-tuning fundamentals (PEFT) and when to fine-tune versus prompt versus use RAG, plus responsible-AI basics.

Domain 2: Using OCI Generative AI Service

Key concepts you must know · 216 practice questions

The fully managed OCI Generative AI service and its pretrained chat models (Cohere Command R, R+, A; Meta Llama 3.x) and embedding models (Cohere Embed).
The Playground for testing prompts, and inference parameters (temperature, top-p, top-k, max tokens, penalties).
Dedicated AI Clusters: fine-tuning clusters (train custom models) versus hosting clusters (serve endpoints), single-tenant and scaled by units.
Fine-tuning methods: T-Few (parameter-efficient, smaller datasets) versus Vanilla, with a JSONL dataset of at least 32 pairs split 80/20.
Creating model endpoints to serve pretrained and custom models for inference.
Security: IAM policies, private endpoints, data isolation in dedicated clusters, and choosing on-demand versus dedicated capacity.

Domain 3: Implement RAG using OCI Generative AI Service

Key concepts you must know · 174 practice questions

The retrieval-augmented generation workflow: retrieve, augment, and generate, and why it grounds responses.
Document ingestion: loading, splitting, and chunking documents with size and overlap tradeoffs.
Creating embeddings of chunks with the OCI Generative AI embedding model.
Storing and indexing vectors in Oracle Database 23ai with AI Vector Search, and similarity search to retrieve top-k chunks.
Generating a grounded answer by feeding retrieved context plus the query to the chat model.
Building RAG and a conversational chatbot with LangChain: prompt templates, chains, memory, loaders, splitters, and vector stores.

Domain 4: Using OCI Generative AI RAG Agents Service

Key concepts you must know · 144 practice questions

The fully managed OCI Generative AI Agents service and what a RAG agent is (retrieve for accuracy, generate for fluency).
Creating knowledge bases from OCI Object Storage or bring-your-own vector search (Oracle Database 23ai, MySQL HeatWave, OCI OpenSearch).
Creating, deploying, and invoking an agent as a multi-turn chatbot with source attribution.
Agent tools: the RAG Tool (hybrid search), the SQL Tool (natural language to SQL), the Agent Tool (multi-agent), and custom function and API tools.
Guardrails: content moderation, prompt-injection protection, and PII protection at agent endpoints.
Custom instructions and optional human-in-the-loop monitoring.

OCI Generative AI Professional (1Z0-1127-25) exam tips

Using OCI Generative AI Service is the heaviest domain. Know the pretrained model families (Cohere Command and Meta Llama), the difference between fine-tuning and hosting dedicated clusters, and T-Few versus Vanilla fine-tuning, including the 32-pair JSONL and 80/20 split.
Master the decision between fine-tuning, RAG, and prompt engineering - the exam repeatedly asks which fits a scenario. RAG grounds answers in your data without retraining; fine-tuning adapts the model; prompting is the lightest option.
For RAG, know the pipeline end to end: chunk, embed, store and index vectors in Oracle Database 23ai, retrieve by similarity, then generate. This exam uses Oracle Database 23ai (not newer branding) and the Cohere and Llama 3.x model families.
For the Agents service, distinguish the tools: the RAG Tool retrieves from a knowledge base with hybrid search and source attribution, the SQL Tool converts natural language to SQL, and the Agent Tool orchestrates multiple agents. Know the knowledge-base source options.
Understand decoding parameters precisely: higher temperature and top-p make output more diverse and less deterministic; greedy and low temperature make it more predictable. These are common single-fact questions.

Study guide FAQ

How many questions are on the exam and what is the passing score?

There are about 50 scored, scenario-based multiple-choice questions in 90 minutes, with a passing score around 68 percent. The exam is delivered through Pearson VUE online or at a test center, costs 245 USD, and the certification is valid for two years.

Do I need programming experience?

There is no formal prerequisite, but Oracle recommends familiarity with Python and basic machine-learning and deep-learning concepts. The exam is scenario-based around building LLM and RAG applications on OCI, so comfort with code helps even though you do not write large programs on the exam.

Which OCI services and tools are covered?

The OCI Generative AI service (pretrained Cohere Command and Meta Llama models, dedicated AI clusters, fine-tuning), retrieval-augmented generation with Oracle Database 23ai vector search, LangChain, and the OCI Generative AI Agents service with its RAG, SQL, and agent tools.

What changed in the 2025 version?

Oracle updated the exam to cover new service features and simplified it based on feedback. The most notable addition is a full domain on the OCI Generative AI RAG Agents service, which became generally available after the 2024 exam was authored.

Is CertGrid practice official Oracle material?

No. CertGrid is an independent practice platform and is not affiliated with or endorsed by Oracle. These questions are original, and they are written to the objectives of 1Z0-1127-25, OCI Generative AI Professional. Read the note under Official exam sources below before you rely on this guide: Oracle's listing no longer carries 1Z0-1127 in any version, and the professional generative-AI exam is now 1Z0-1158-26, OCI Enterprise AI Professional, which covers substantially different ground. Confirm the current exam topics on Oracle's certification site before you book.

Official exam sources

A current vendor blueprint exists and the domains on this page do not yet match it. They follow the structure our question bank is built to; the note below says exactly where the two differ, so the gap can be seen rather than guessed at.

Oracle - OCI Enterprise AI Professional (1Z0-1158-26)link and content verified 8 September 2026Oracle's current professional-level generative-AI exam, and the only one in its certification listing. It publishes "Duration 90 Minutes", "Number of Questions 50" and "Passing Score 68%", which are exactly the three figures this guide states - so our numbers are right even though the exam they belong to has moved on. Read over the DevTools protocol: mylearn.oracle.com is a single-page app that keeps enough network activity alive that Chrome's --dump-dom never returns, so the page is navigated, given a fixed wall-clock wait, and read from document.innerText. Public page, no sign-in.

This page and the current vendor source differ. This is the largest Oracle finding and nothing has been relabelled. Our guide is exam 1Z0-1127-25, OCI Generative AI Professional. Oracle's certification listing contains no 1Z0-1127 in any version; the professional generative-AI slot is now 1Z0-1158-26, OCI Enterprise AI Professional, marked NEW. The content has moved substantially, not just the code: Oracle's six domains are Fundamentals of Large Language Models, Fundamentals of Agentic AI, OCI Enterprise AI Models, OCI Enterprise AI Agents, Build and Deploy Hosted Agents, and Governance and Pricing, against our four - Fundamentals of Large Language Models, Using OCI Generative AI Service, Implement RAG using OCI Generative AI Service, Using OCI Generative AI RAG Agents Service. Only the first domain survives unchanged. The new exam expects the agent loop, MCP, the Responses API and its Code Interpreter / File Search / MCP Calling / Function tools, packaging agents as container images, guardrails and prompt-injection defence, PII handling and cost management - none of which our RAG-era bank was written for. So this is a re-scope: it needs new questions before the title or code can move.

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