Oracle Cloud Infrastructure 2024 Generative AI Professional (1Z0-1127-24) Practice Exam

About Oracle Cloud Infrastructure 2024 Generative AI Professional (1Z0-1127-24) Exam

The Oracle Cloud Infrastructure 2024 Generative AI Professional certification is designed for Software Developers, Machine Learning/AI Engineers, and Generative AI Professionals who have a foundational understanding of Machine Learning and Deep Learning concepts, along with familiarity with Python and Oracle Cloud Infrastructure (OCI).

Knowledge Gained

Professionals who earn this certification will have a solid understanding of Large Language Model (LLM) architecture and proficiency in using OCI Generative AI Services, such as Retrieval-Augmented Generation (RAG) and LangChain, to build, trace, evaluate, and deploy LLM applications.

Exam Details 

• Exam Name: Oracle Cloud Infrastructure 2024 Generative AI Professional 
• Exam Code: 1Z0-1127-24
• Format: Multiple Choice Questions 
• Duration: 90 Minutes
• Number of Questions: 40
  
• Passing Score: 65% and above

Exam Objectives

The Oracle Cloud Infrastructure 2024 Generative AI Professional (1Z0-1127-24) Exam covers the following topics - 

Module 1 - Explain Fundamentals of Large Language Models (LLMs) - 20%

• Explain the fundamentals of LLMs
• Understand LLM architectures
• Design and use prompts for LLMs
• Understand LLM fine-tuning
• Grasp the fundamentals of code models, multi-modal models, and language agents

Module 2 - Learn about Using OCI Generative AI Service - 45%

• Explain the fundamentals of OCI Generative AI Service
  
• Use pre-trained foundational models for generation, summarization, and embedding
• Create dedicated AI clusters for fine-tuning and inference
• Fine-tune base models with custom datasets
• Create and use model endpoints for inference
• Explore OCI Generative AI security architecture

Module 3 - Learn about Building an LLM Application with OCI Generative AI Service - 35%

• 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