Overview

Exam StructureFundamentals of LLMs- Explain the fundamentals of LLMs- Understand LLM architectures- Design and use prompts for LLMs- Understand LLM fine-tuning- Understand code models, multi-modal, and language agentsUsing 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 architectureBuilding an LLM Application- 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 applicationTest-Taking StrategiesStrategy Details Time Management: Allocate specific times for each section; use practice exams to gauge timing.Answering Techniques: Read questions carefully, eliminate clearly wrong answers, and manage your guesses.Practice Exams: Take full-length practice exams to simulate real exam conditions.Review Mistakes: Analyze incorrect answers to understand mistakes and improve.Agile and Hybrid Approaches: Understand agile methodologies and hybrid approaches applicable to AI development.Comprehensive Practice Exams: Full-length practice exams to prepare for the actual exam conditions.Effective Preparation Techniques: Study methods, resources, and strategies to maximize learning and retention.

Skills

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