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Certification

Certification in Developing Agentic Python Coding Agents with Google Gemini API

Get certified in Agentic AI in Python with the Google Gemini API. Prove you can build a safe CLI coding agent that plans, edits files, runs tests, fixes bugs, adds features, and explains changes using a clear tool schema, path limits, and timeouts.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate · Expert, technical
IT and developmentGoogle
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

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What the exam covers

2 questions from each of the 15 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Why build an agent6:03
  2. 02Four tools and setup9:41
  3. 03Gemini API basics9:00
  4. 04CLI arguments and history5:55
  5. 05Verbose flag and test project5:44
  6. 06Tool 1: get files info13:16
  7. 07Tool 2: get file content8:45
  8. 08Tool 3: write file and security9:06
  9. 09Tool 4: run Python file10:30
  10. 10The system prompt15:10
  11. 11Declaring functions8:28
  12. 12All declarations7:41
  13. 13Function calling logic8:11
  14. 14The agentic loop9:41
  15. 15Final demo: fixing a bug7:12
About the course

Agentic AI in Python: Build a Coding Agent with Google Gemini API (Video Course) is a certification that guides you to build a real Python coding agent that plans, acts, and iterates, culminating in a CLI that reads/writes files, runs tests, fixes bugs, adds features, and explains changes with built-in safety.

By mastering agent design, safe tool use, and reliable automation, you gain Increased Productivity, Competitive Advantage, Improved Decision-Making, Adaptability and Growth, and a Future-Proof Career with Higher Income Potential.

Enroll now to turn modern LLM capabilities into a production-ready agent and earn a credential that signals practical, job-ready skill.

This certification covers the following topics:

  • Agents vs Chatbots: Core Concepts and Terminology
  • System Architecture: Planning, Acting, and Iterating
  • Project Setup with uv and Gemini API Integration
  • Designing LLM-Friendly Tools and Clear Tool Schemas
  • Function Declarations: Teaching Gemini About Your Tools
  • System Prompts, Roles, and Message History
  • Implementing the Agentic Loop in Python (main.py)
  • Safe Execution: Path Limits, Timeouts, and Guardrails
  • Reliability: Interfaces, Error Handling, and Recovery Patterns
  • Token Management, Cost Control, and Test-Driven Workflows
  • Extending the Agent and Production Hardening