DEV Community

YOGARATHNAM-S
YOGARATHNAM-S Subscriber

Posted on

Generative AI Leader Preparation 2025

🧠 Google Cloud Generative AI Leader Certification Personal Notes

Personal notes from the Google Cloud Generative AI Leader Certification, based on the Cloud Skills Boost Generative AI Leader path.

📘 Exam Overview

Category Weight
Fundamentals of Generative AI ~30%
Google Cloud's Generative AI Offerings ~35%
Techniques to Improve Model Output ~20%
Business Strategies for Successful Gen AI ~15%

🔗 Helpful Resources

1. 📊 Data and Machine Learning Fundamentals

🔍 Data as the Foundation of AI

Data quality dimensions:

  • Accuracy
  • Completeness
  • Consistency
  • Relevance
  • Availability
  • Cost
  • Format

"Understanding the types and quality of your data is crucial for successful AI initiatives."

🧠 Machine Learning Approaches

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

"Approach depends on task and data."

🔄 ML Lifecycle

  • Data ingestion & preparation
  • Model training
  • Deployment
  • Management

🛠️ Google Cloud Tools:

  • Vertex AI
  • Dataflow, BigQuery, Cloud Storage

2. ⚙️ Model Development with Vertex AI

🧪 Model Training

  • Managed training environments
  • Prebuilt containers
  • Custom jobs
  • Evaluation tools
  • Accelerated compute

🚀 Model Deployment

  • Scalable infrastructure
  • Easy deployment for predictions

🗂️ Model Management

  • Versioning
  • Drift & performance monitoring
  • Feature Store
  • Model Garden
  • Vertex AI Pipelines

3. 🌐 Foundation Models & Generative AI

🤖 What is Generative AI?

  • Builds on foundation models
  • Uses deep learning
  • Capable of generating new content

🧰 Vertex AI for Gen AI

  • Easy access & customization
  • Empowers business applications

🧬 Google-Developed Models

Model Description
Gemini Multimodal (text, images, audio, video)
Gemma Lightweight open-source models
Imagen Text-to-image generation
Veo Text-to-video generation

🧠 Model Selection Criteria

  • Modality
  • Context window
  • Security
  • Availability
  • Cost
  • Performance
  • Fine-tuning
  • Integration

4. ❗ Limitations of Foundation Models

  • Data Dependency
  • Knowledge Cutoff
  • Bias & Fairness
  • Hallucinations
  • Edge Cases

5. 🛠️ Techniques to Overcome Limitations

📌 Grounding

  • Connect output to verifiable sources
  • Reduces hallucinations
  • Increases trust

🔎 Retrieval-Augmented Generation (RAG)

  • Search → Augment → Generate
  • More accurate and context-aware results

🧾 Prompt Engineering

  • Craft precise instructions
  • Limited by model’s knowledge

🧪 Fine-Tuning

  • Custom training on task-specific data
  • Specialized model behavior

6. 🙋 Humans in the Loop (HITL)

Use Cases:

  • Content moderation
  • Sensitive fields (health, finance)
  • High-risk decisions
  • Pre/Post generation review

7. 🔐 Secure AI

  • Protect models from misuse
  • Risks: Data poisoning, Model theft, Prompt injection

🛡️ SAIF Framework for secure lifecycle

8. ⚖️ Responsible AI

Transparency

  • Clear model operations & data usage

Privacy

  • Anonymization, pseudonymization

Data Quality & Fairness

  • Garbage in, garbage out
  • Biased input leads to unfair output

Accountability & Explainability

  • Explainable AI tools
  • Legal compliance: privacy, bias, liability

9. 🤖 Agents & Gen AI Applications

Agent Capabilities

  • Understand language
  • Automate tasks
  • Personalize responses

Agent Workflows

💬 Conversational Agents

  1. Input
  2. Understand
  3. Call Tool
  4. Generate
  5. Deliver

⚙️ Workflow Agents

  1. Input
  2. Understand
  3. Call Tool
  4. Generate Result
  5. Deliver

Prompting Frameworks

  • ReAct: Reason + Act + Observe
  • CoT: Step-by-step thinking

10. 🔧 Vertex AI MLOps Tools

  • Feature Store
  • Model Registry
  • Evaluation Tools
  • Vertex Pipelines
  • Monitoring

11. 🏗️ Building Models with Vertex AI

  • Fully Custom: Any framework
  • AutoML: No-code, guided model creation

12. 📱 Gemini Nano

  • Runs locally on edge devices
  • Tools: Lite Runtime (LiteRT), Gemini Nano

13. 🧰 Gemini for Google Workspace

  • Available in Gmail, Docs, Sheets, Meet, Slides
  • Plan-based features 🔗 Learn more

14. 🧾 Prompting Techniques

  • Zero-shot: No example
  • One-shot: One example
  • Few-shot: Several examples

🎭 Role Prompting

  • Assign a persona (e.g., Shakespeare, analyst)

⛓️ Prompt Chaining

  • Multi-step, sequential prompts

15. 📓 NotebookLM

  • AI-first notebook grounded in your docs
  • Summarize, draft, ask questions
  • Plus: Customization & analytics
  • Enterprise: Privacy, IAM controls 🔗 Learn more

16. ⚙️ Sampling Parameters

  • Token Count
  • Temperature: Creativity
  • Top-p: Focus on likely tokens
  • Safety Filters
  • Output Length

17. 🔄 Google AI Studio vs Vertex AI Studio

Feature Google AI Studio Vertex AI Studio
Audience Experimenters Production Developers
Use Case Quick API Tests End-to-end ML lifecycle

18. 🧠 Advanced Prompting Techniques

ReAct Framework

  • Think → Act → Observe → Respond
  • Reduces hallucinations, increases trust

Chain-of-Thought (CoT)

  • Step-by-step reasoning
  • Improves accuracy

19. 🔁 Reasoning Loop (ReAct)

  1. Think
  2. Act
  3. Observe
  4. Repeat

20. 🔍 RAG with Tools

  • Retrieval: Search, vectors, graphs
  • Augmentation: Add context to prompt
  • Generation: Produce better answers

21. 🧾 Conversational Agents & Playbooks

  • Step-by-step interaction design
  • Use with external tools & APIs

22. 🧭 Metaprompting

  • Dynamic, adaptable prompts
  • AI designs its own prompts

23. 🧠 Agentspace

Feature NotebookLM Agentspace
Purpose Analyze your documents Org-wide AI assistant
Scope Only user uploads All connected systems
Integration NotebookLM Enterprise Enterprise dashboards, automation

📚 More Resources

Top comments (0)