My Journey to Becoming a Google Generative AI Leader

by Selwyn Davidraj     Posted on September 14, 2025

My Journey to Becoming a Google Generative AI Leader

Sharing my journey of preparing for and completing the Google Generative AI Leader certification.
In this post, I cover key takeaways, practical tips, and reflections.

My Journey to Becoming a Google Generative AI Leader

Recently, I had the opportunity to complete the Google Generative AI Leader certification—an experience that I thoroughly enjoyed and found extremely rewarding. I wanted to share my preparation journey, key exam topics, and some tips for anyone planning to pursue this path.

Why I Took the Certification

Over the past couple of years, I’ve been deeply engaged in the AI and ML space, exploring use cases, frameworks, and hands-on projects around Agentic AI, RAG patterns, and AI in Cloud/SRE. Naturally, when Google introduced this certification, it felt like the perfect way to formalize my knowledge, validate my understanding, and benchmark myself against industry standards.

Preparation Journey

My prep was a mix of structured learning and hands-on exploration:

  • Google Cloud Skill Boosts: Followed the official courses and labs to get a strong foundation.
  • Hands-on Projects: Tried applying GenAI to real-world use cases—college research, DevOps automation, and observability workflows.
  • Documentation & Whitepapers: Read through Google’s Generative AI docs, best practices, and case studies.
  • Community Discussions: Engaged in LinkedIn groups and forums to understand how others are applying these concepts in enterprise environments.

Key Topics Covered

The exam tested both breadth and depth across generative AI concepts. Some of the major areas included:

  • Foundations of Generative AI, LLMs, and Transformer models
  • Responsible AI principles: ethics, fairness, and governance
  • GenAI in Google Cloud: Vertex AI, Model Garden, and Generative AI Studio
  • Business perspectives: identifying use cases, aligning GenAI with strategy, and measuring ROI & impact
  • Emerging patterns like Retrieval-Augmented Generation (RAG), Agentic AI workflows, and integration with cloud-native architectures

Tips for Future Exam Takers

If you’re planning to take this certification, here are a few tips from my experience:

  • Balance theory with practice – Don’t just memorize; actually try small projects with Vertex AI or Bedrock-style platforms.
  • Understand Responsible AI deeply – Questions are not just about tech but also governance and ethics.
  • Think like a leader – Many scenarios focus on decision-making and business alignment, not just technical implementation.
  • Use Google Cloud resources – The official training paths and practice assessments are gold.

How I Felt About the Experience

For me, this certification wasn’t just an exam—it was a journey of reflection. I enjoyed connecting the dots between technical expertise and leadership responsibilities. It reinforced my belief that AI leaders are not only builders but also translators of technology into business value.

What’s Next?

This is just the beginning. I plan to pursue more advanced certifications in the AI/ML and Cloud leadership track. More importantly, I’m committed to helping others who want to explore this journey—whether it’s sharing study resources, mentoring, or simply answering questions.

So, if you’re preparing for the Google Generative AI Leader certification (or thinking about it), feel free to reach out. I’d be glad to share my learnings and support you along the way.

⭐ Closing Note:
The world of Generative AI is moving fast, and staying ahead requires both curiosity and structured learning. This certification gave me the confidence to lead AI conversations with clarity with my customers and I’m excited about the road ahead.