Oracle Cloud Infrastructure 2025 AI Foundations Associate
1Z0-1127-25
Oracle Cloud Infrastructure 2025 AI Foundations Associate
Money Back Guarantee
98% Success Rate
Real Questions
1
Fundamentals of Large Language Models (LLMs) 20%
1.1
Explain the fundamentals of LLMs
2 subtopics1.1.1LLM Architecture Fundamentals
1.1.2Token Processing and Embeddings
Learning Objectives
- Understand the basic architecture of Large Language Models
- Explain how transformers process text data
- Describe tokenization and embedding concepts
1.2
Understand LLM architectures
2 subtopics1.2.1Transformer Model Components
1.2.2Pre-trained Model Types
Learning Objectives
- Describe detailed transformer architecture components
- Compare different pre-trained model types
- Select appropriate models for specific use cases
1.3
Design and use prompts for LLMs
2 subtopics1.3.1Prompt Engineering Techniques
1.3.2Model Parameters for Prompt Control
Learning Objectives
- Design effective prompts for various LLM tasks
- Configure model parameters to control output quality
- Apply prompt engineering best practices
1.4
Understand LLM fine-tuning
2 subtopics1.4.1Fine-tuning Methodologies
1.4.2Parameter-Efficient Fine-Tuning (PEFT)
Learning Objectives
- Explain different fine-tuning approaches for LLMs
- Calculate training parameters like totalTrainingSteps
- Understand parameter-efficient fine-tuning methods
1.5
Understand the fundamentals of code models, multi-modal, and language agents
3 subtopics1.5.1Code Generation Models
1.5.2Multi-modal Models
1.5.3Language Agents
Learning Objectives
- Understand specialized LLM applications (code, vision, etc.)
- Explain multi-modal model capabilities and limitations
- Describe language agent architectures and patterns
Domain Hands-on Skills
Designing effective prompts for various tasksConfiguring model parameters (temperature, top-p, etc.)Understanding LLM architectures and their components
Common Mistakes to Avoid
- Confusing encoder-only vs decoder-only architectures
- Misunderstanding temperature parameter effects
- Incorrect calculation of totalTrainingSteps formula
2
Using OCI Generative AI Service 40%
3
Implement RAG using OCI Generative AI service 20%
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