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Certification in Developing Intelligent Java Applications with Spring AI and LLMs

Get certified in Spring AI for Java Developers and showcase your ability to design, build, and deploy intelligent apps,integrating LLMs for chatbots, automation, and smart features using familiar Java and Spring tools.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate
IT and developmentProduct development
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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. 01Introduction and setup18:30
  2. 02First app and AI concepts23:40
  3. 03Prompt engineering and Spring AI overview20:35
  4. 04Chat clients, streaming and system messages25:55
  5. 05Structured output and multimodal29:50
  6. 06Chat memory13:50
  7. 07Limitations, guarding and prompt stuffing20:10
  8. 08RAG19:45
  9. 09Tools and function calling23:25
  10. 10MCP servers29:40
  11. 11Open source and local models33:10
  12. 12Observability22:45
  13. 13Testing and evaluations15:15
  14. 14Deterministic vs non-deterministic testing26:00
  15. 15AI portfolio and wrap-up15:10
About the course

The "Spring AI for Java Developers: Build Intelligent Apps with Modern LLMs (Video Course)" certification empowers Java professionals to seamlessly integrate advanced AI capabilities into their Spring applications. Gain practical skills for increased productivity, enhanced adaptability, and a future-proof career by leveraging large language models without leaving your existing tech stack. Enroll now to unlock the potential of AI-driven features and stand out in the evolving software landscape.

This certification covers the following topics:

  • Foundational AI Concepts for Java and Spring Developers
  • Prompt Engineering for Effective AI Interactions
  • Cost Management and Tokenization in LLM Usage
  • Criteria for Selecting and Evaluating AI Models
  • Spring AI Development Workflow and Key Features
  • Integrating Open-Source and Proprietary LLMs
  • Implementing Observability for AI-Powered Applications
  • Testing and Evaluating AI Model Performance
  • Designing and Deploying AI-Driven Solutions with Spring
  • Building Chatbots and Automation with Modern LLMs