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OCI GenAI Professional
Participants learn LLM fundamentals, RAG architecture, fine‑tuning, OCI GenAI services, AI agents, and responsible AI practices, gaining the expertise to design, deploy, and manage generative AI solutions on Oracle Cloud.
90
Minutes
50
Questions
65
Passing Score
$245
Exam Cost
Who Should Take This
Cloud architects, data engineers, and AI specialists who have at least two years of experience with OCI and machine‑learning pipelines are ideal candidates. They seek certification to validate their ability to implement retrieval‑augmented generation, customize large language models, and ensure responsible, production‑ready AI deployments.
What's Covered
1
LLM Fundamentals and Architecture
2
RAG Architecture
3
Fine-Tuning and Customization
4
OCI GenAI Service and AI Agents
5
Responsible AI and Production Operations
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
62 learning goals
1
LLM Fundamentals and Architecture
2 topics
Transformer and LLM Architecture
- Apply transformer architecture knowledge including self-attention, multi-head attention, and positional encoding to understand LLM behavior.
- Design prompt engineering strategies with zero-shot, few-shot, chain-of-thought, and system prompt patterns for optimal LLM outputs.
- Implement LLM inference parameter tuning with temperature, top-p, top-k, frequency penalty, and stop sequences for output control.
- Analyze LLM output quality to identify hallucinations, factual errors, and bias issues requiring prompt or model adjustments.
- Configure Transformer and LLM Architecture with appropriate settings, policies, and parameters for a production deployment scenario.
- Assess Transformer and LLM Architecture implementations against best practices to identify gaps and recommend improvements for production readiness.
- Architect Transformer and LLM Architecture solutions with scalability patterns, capacity planning, and growth accommodation for long-term sustainability.
Model Types and Selection
- Evaluate foundation model options in OCI GenAI including Cohere Command, Cohere Embed, and Meta Llama for different task requirements.
- Design model selection frameworks based on task type, latency requirements, accuracy needs, and cost constraints for enterprise use cases.
- Implement model benchmarking and evaluation using automated metrics (BLEU, ROUGE, perplexity) and human evaluation protocols.
- Architect multi-model strategies using different foundation models for different tasks within a single application pipeline.
- Evaluate Model Types and Selection alternatives and tradeoffs to recommend the optimal approach for given performance and cost constraints.
- Formulate Model Types and Selection governance frameworks with policies, standards, and compliance monitoring for organizational alignment.
- Explain how to troubleshoot common issues with Model Types and Selection including error messages, logs, and diagnostic procedures.
2
RAG Architecture
2 topics
RAG Design Patterns
- Design RAG architectures with document ingestion, chunking strategies, embedding generation, vector storage, and retrieval pipelines.
- Implement vector search using Oracle Database 23ai AI Vector Search with HNSW indexes for efficient similarity retrieval.
- Architect document processing pipelines with chunking (fixed, semantic, recursive), metadata extraction, and embedding computation.
- Analyze RAG retrieval quality using precision, recall, and relevance scoring to optimize chunking and embedding strategies.
- Design enterprise-grade RAG Design Patterns architectures incorporating high availability, disaster recovery, and security requirements.
- Apply RAG Design Patterns configuration patterns to meet specific business requirements including compliance and governance needs.
Advanced RAG Patterns
- Design hybrid search combining vector similarity, keyword (BM25), and metadata filtering for improved retrieval accuracy.
- Implement RAG with re-ranking using cross-encoder models to improve retrieved document relevance before LLM generation.
- Architect multi-index RAG with specialized indexes for different document types, languages, or knowledge domains.
- Evaluate RAG system performance including retrieval accuracy, generation quality, latency, and cost optimization tradeoffs.
- Implement Advanced RAG Patterns following best practices for security, performance, and reliability in Oracle Cloud Infrastructure Generative AI Professional.
- Diagnose Advanced RAG Patterns issues by analyzing metrics, logs, and configuration to determine root causes and remediation steps.
3
Fine-Tuning and Customization
2 topics
Fine-Tuning Techniques
- Design fine-tuning strategies selecting between full fine-tuning, LoRA, QLoRA, and prompt tuning based on data and resource constraints.
- Implement OCI GenAI fine-tuning with training data preparation, hyperparameter configuration, and validation metric monitoring.
- Architect training data pipelines with data cleaning, deduplication, format conversion, and quality filtering for fine-tuning datasets.
- Analyze fine-tuned model performance comparing base versus fine-tuned outputs on domain-specific evaluation benchmarks.
- Analyze Fine-Tuning Techniques configurations to identify security vulnerabilities, performance bottlenecks, and optimization opportunities.
- Recommend Fine-Tuning Techniques optimization strategies balancing performance, cost, operational complexity, and risk management.
Custom Model Deployment
- Design dedicated AI cluster configurations for hosting fine-tuned models with GPU allocation and endpoint management.
- Implement model versioning and A/B testing between base and fine-tuned models for incremental quality improvement validation.
- Architect model serving with OCI GenAI dedicated endpoints, auto-scaling, and private network access for production deployments.
- Evaluate fine-tuning ROI comparing custom model quality improvements against training cost and maintenance overhead.
- Architect Custom Model Deployment solutions with scalability patterns, capacity planning, and growth accommodation for long-term sustainability.
- Configure Custom Model Deployment with appropriate settings, policies, and parameters for a production deployment scenario.
4
OCI GenAI Service and AI Agents
2 topics
OCI GenAI Service
- Implement OCI GenAI API integration with generation, summarization, and embedding endpoints for application development.
- Design OCI GenAI Agents with knowledge bases, tool definitions, and conversational memory for autonomous task completion.
- Architect GenAI integration with Oracle Fusion applications, APEX, and Oracle Database for enterprise AI-powered features.
- Analyze GenAI service metrics including token throughput, latency, error rates, and cost per request for capacity planning.
- Explain how to troubleshoot common issues with OCI GenAI Service including error messages, logs, and diagnostic procedures.
- Evaluate OCI GenAI Service alternatives and tradeoffs to recommend the optimal approach for given performance and cost constraints.
AI Agent Architecture
- Design AI agent frameworks with tool use, function calling, planning loops, and memory management for complex task orchestration.
- Implement agent tool integrations connecting GenAI with database queries, API calls, file operations, and workflow automation.
- Architect multi-agent systems with specialized agents for different domains coordinated through orchestration frameworks.
- Evaluate agent architectures to identify reliability issues, improve task completion rates, and optimize agent interaction patterns.
- Compare AI Agent Architecture deployment patterns to determine the best architecture for meeting availability and scalability requirements.
- Design enterprise-grade AI Agent Architecture architectures incorporating high availability, disaster recovery, and security requirements.
5
Responsible AI and Production Operations
2 topics
Responsible AI
- Design responsible AI guardrails with content filtering, safety classifiers, and output validation for production GenAI applications.
- Implement bias detection and mitigation strategies for GenAI systems including prompt debiasing and output monitoring.
- Architect data privacy controls for GenAI including PII detection, data anonymization, and consent management in training data.
- Analyze ethical implications of GenAI deployments to recommend governance policies, disclosure requirements, and use case limitations.
- Plan Responsible AI migration and modernization strategies including phased rollout, testing, and rollback procedures.
- Implement Responsible AI following best practices for security, performance, and reliability in Oracle Cloud Infrastructure Generative AI Professional.
GenAI Operations
- Design GenAI monitoring with token usage tracking, cost management, quality metrics, and performance dashboards for production systems.
- Implement caching strategies for GenAI with semantic caching, response deduplication, and TTL management for cost optimization.
- Architect GenAI security with prompt injection prevention, output sanitization, and rate limiting for production API protection.
- Evaluate GenAI system performance to identify quality degradation, cost anomalies, and scaling requirements for optimization.
- Deploy GenAI Operations with integration to monitoring, logging, and alerting services for operational visibility.
- Analyze GenAI Operations configurations to identify security vulnerabilities, performance bottlenecks, and optimization opportunities.
Scope
Included Topics
- All domains in the Oracle Cloud Infrastructure Generative AI Professional (1Z0-1127) exam guide.
- Topics: LLMs, RAG Architecture, Fine-Tuning, OCI GenAI Service, Prompt Engineering, AI Agents, Vector Search, Responsible AI.
- Oracle Cloud Infrastructure services, tools, and best practices relevant to this certification.
- Scenario-based problem solving at the Professional level.
Not Covered
- Topics outside the official exam guide scope.
- Programming language specifics and code-level implementation details.
- Specific pricing values and promotional offers that change over time.
- Third-party products and non-Oracle services beyond basic integration awareness.
Official Exam Page
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