Jnachi Certified Python for AI & Prompt Engineering
Validates Python engineers and AI practitioners on integrating state-of-the-art LLMs into production applications. Assesses OpenAI/Anthropic SDK usage, streaming, structured JSON extraction via Pydantic, vector database embeddings, tool/agent orchestration, and token cost optimization.
Jnachi Certified Python for AI & Prompt Engineering
Validates Python engineers and AI practitioners on integrating LLMs into production applications, including OpenAI/Anthropic SDKs, Pydantic structured extraction, function calling, agent loops, RAG, and token cost optimization.
Exam Competency Matrix & Domain Breakdown
Select any domain below to inspect tested skills, real-world scenarios, and preparation materials.
LLM APIs, Structured Outputs & Streaming
Mastery of official Python SDKs, streaming token generators, Pydantic schema constraints, and prompt formatting.
Configuring client sessions, temperature, top_p, seeds, and system/user message orchestration.
Using BaseModel schemas to guarantee runtime JSON validation and type safety.
Portal Registration Required Before Exam
Your full name, location, and organization are verified and printed directly on your official diploma.
Exam Objectives & Tested Competencies
LLM APIs (OpenAI/Anthropic) & Streaming in Python
Evaluated in scenario-based proctored questions.
Structured Outputs, Pydantic & JSON Validation
Evaluated in scenario-based proctored questions.
Function Calling, Tool Execution & Agentic Loops
Evaluated in scenario-based proctored questions.
RAG Pipelines, Vector Embeddings & Token Optimization
Evaluated in scenario-based proctored questions.
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