1Z0-1127-24: Become an OCI Generative AI Professional 2024

3-Full Length Practice Test for OCI Generative AI Professional 2024

The Oracle Cloud Infrastructure 2024 Generative AI Professional (1Z0-1127-24) certification is designed for Software Developers, Machine Learning/AI Engineers, Gen AI Professionals who have a basic understanding of Machine Learning and Deep Learning concepts, familiarity with Python and OCI.

Students who earn this credential have a strong understanding of the Large Language Model (LLM) architecture and are skilled at using OCI Generative AI Services, such as RAG and LangChain, to build, trace, evaluate, and deploy LLM applications.

Skills you will learn:

• Fundamentals of Large Language Models (LLMs): LLM basics, LLM architectures, Prompt Engineering, Fine-tuning techniques, fundamentals of code models, Multi-modal LLMs and Language Agents

• OCI Generative AI Deep-Dive: Pretrained Foundational Models (Generation, Summarization, Embedding), Flexible Fine-tuning including T-Few techniques, Model Inference, Dedicated AI Clusters, Generative AI Security architecture

• Build a Conversational Chatbot with OCI Generative AI: Understand RAG, Vector Databases, Semantic Search, build chatbot using LangChain Framework (Prompts, Models, Memory, Chains), Trace and Evaluate chatbot and deploy on OCI

Fundamentals of Large Language Models (LLMs)

• Explain the fundamentals of LLMs

• Understand LLM architectures

• Design and use prompts for LLMs

• Understand LLM fine-tuning

• Understand the fundamentals of code models, multi-modal, and language agents

Using OCI Generative AI Service

• Explain the fundamentals of OCI Generative AI service

• Use pretrained foundational models for Generation, Summarization, and Embedding

• Create dedicated AI clusters for fine-tuning and inference

• Fine-tune base model with custom dataset

• Create and use model endpoints for inference

• Explore OCI Generative AI security architecture

Building an LLM Application with OCI Generative AI Service

• Understand Retrieval Augmented Generation (RAG) concepts

• Explain vector database concepts

• Explain semantic search concepts

• Build LangChain models, prompts, memory, and chains

• Build an LLM application with RAG and LangChain

• Trace and evaluate an LLM application

• Deploy an LLM application