A comprehensive study guide covering all official objectives for the OCI Generative AI Professional certification exam.
This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.
18 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.
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View exam guideIntroduction to AI and Machine Learning Concepts
Objective 1.1 · Describe AI, ML, deep learning, generative AI, and MLOps concepts
Overview of OCI AI Services
Objective 1.2 · Describe OCI AI services and their use cases (AI Vision, AI Language, AI Speech, Document Understanding, Anomaly Detection, AI Forecasting)
Foundation Models and Large Language Models
Objective 2.1 · Explain foundation models (LLMs, LMMs) and their capabilities
Data Science and MLOps on OCI
Objective 2.2 · Describe OCI Data Science and MLOps capabilities
OCI Generative AI Service Architecture
Objective 3.1 · Describe the architecture and components of OCI Generative AI service
OCI Generative AI Models and Capabilities
Objective 3.2 · Describe OCI Generative AI models (text generation, summarization, embedding, chat, agent capabilities)
RAG and Fine-Tuning Techniques
Objective 4.1 · Explain Retrieval-Augmented Generation (RAG) and fine-tuning (full, LoRA) techniques
Agents and Tools in Generative AI
Objective 4.2 · Describe AI agents, tool integration, and agent orchestration in OCI Generative AI
Networking and Security for Generative AI
Objective 5.1 · Describe networking and security considerations for OCI Generative AI deployments
Governance and Compliance in Generative AI
Objective 5.2 · Explain data governance, compliance, and responsible AI principles in OCI Generative AI
Performance Optimization and Cost Management
Objective 5.3 · Describe performance optimization, caching, and cost management strategies for generative AI workloads
OCI Generative AI Playground and API
Objective 3.3 · Demonstrate using OCI Generative AI Playground and API (including SDKs and CLI) for inference
Use Cases for OCI Generative AI
Objective 3.4 · Identify common use cases for OCI Generative AI (customer service, content creation, code generation, etc.)
Integrating OCI Generative AI with Other Services
Objective 4.3 · Explain how to integrate OCI Generative AI with OCI Search, OCI Database, OCI Data Flow, and other OCI services
Deploying and Monitoring Generative AI Models
Objective 4.4 · Describe deployment options and monitoring of generative AI models (dedicated AI clusters, endpoints, logging)
Environment Setup on OCI
Objective 6.1 · Set up OCI environments (policies, networking, authentication) for generative AI development
Hands-On Lab: RAG Deployment
Objective 6.2 · Implement a RAG solution using OCI Generative AI and OCI Search
Hands-On Lab: Fine-Tuning and Inference
Objective 6.3 · Fine-tune a model and perform inference using OCI Generative AI
Free 1Z0-1127 practice questions with full explanations. Test what you learn chapter by chapter.
1Z0-1127 Practice Questions