Earlier this month, I appeared for the Google Certified Generative AI Leader exam. In this post, I want to share my exam experience, the learning path I followed, and a clear preparation strategy for anyone planning to take this certification.
Exam Experience
After a long time, instead of taking the exam from home (online-proctored), I opted for an exam center (onsite-proctored) and honestly, I really liked it. The setup was professional, distraction-free, and helped me stay focused throughout the exam.
The exam duration was 90 minutes, with 45 scenario-based questions. The questions were a mix of single-choice and multiple-choice, but the majority were single-choice.
Most of the questions were straightforward and easy to answer, provided you have:
Thoroughly studied the official course content
Basic exposure to Generative AI or agentic AI concepts
Even if you’ve learned GenAI from other vendors, that knowledge still helps—because core concepts remain largely the same regardless of the platform.
I was able to complete and submit the exam a lot early, and I passed. Initially, once you submit the exam it shows only a pass/fail result, which is later officially verified and confirmed via email. Google does not publish the exam score, so I can’t share an exact number—but based on my confidence, it went quite well. There were only a few questions that required deeper thinking.
Preparation Strategy
Now let’s talk about preparation—probably the main reason you’re reading this post.
According to the official exam guide, the certification covers the following areas with approximate weightings:
Fundamentals of Generative AI (~30% of the exam)
Google Cloud’s Generative AI Offerings (~35% of the exam)
Improving Model Output (~20% of the exam)
Business Strategy (~15% of the exam)
Official Resources (Highly Recommended)
I strongly recommend following the official learning path, which is freely available:
Official Exam Guide:
https://services.google.com/fh/files/misc/generative_ai_leader_exam_guide_english.pdfLearning Path:
https://www.skills.google/paths/1951Study Guide:
https://services.google.com/fh/files/misc/generative_ai_leader_study_guide_english.pdf
1. Fundamentals of Generative AI (~30%)
Focus on core concepts and business implications.
Core Concepts
Generative AI: An application of ML that focuses on creating new content
. It is a type of AI that can create new content and ideas . Deep Learning: A subset of ML that uses artificial neural networks with many layers to extract complex patterns from data
. Foundation Models: Powerful ML models trained on massive amounts of unlabeled data, allowing them to develop a broad understanding of the world
. They are the basis of Gen AI and are adaptable to many tasks . Large Language Models (LLMs): A type of foundation model that is designed to understand and generate human language
. Multimodal Models: Gen AI applications that can process and generate different types of data like text, images, and code simultaneously
. Diffusion Models: A type of model (like Imagen) that generates high-quality images from textual descriptions
. Prompting: The method of interacting with foundation models and guiding them by providing instructions or inputs to generate desired outputs
. Prompt Engineering: The art and science of creating effective inputs (prompts) for generative AI models to maximize their value and tailor responses to specific needs
.
Machine Learning Approaches
Supervised Learning: Trains models on labeled data to predict outputs for new inputs
. Unsupervised Learning: Uses unlabeled data to find natural groupings and patterns
. Reinforcement Learning: Learns through interaction and feedback to maximize rewards and minimize penalties
.
ML Lifecycle Stages
Data Ingestion and Preparation: The process of collecting, cleaning, and transforming raw data into a usable format for analysis or model training
. Model Training: The process of creating your ML model using data
. Model Deployment: The process of making a trained model available for use
. Model Management: The process of managing and maintaining your models over time
.
Data Concepts
Structured Data: Data that is organized and easy to search, often stored in relational databases
. Unstructured Data: Data that lacks a predefined structure and requires sophisticated analysis techniques (e.g., text descriptions, images, audio)
. Labeled Data: Data that has associated tags, such as a name, type, or number
. Unlabeled Data: Raw, unprocessed information that hasn't been tagged and lacks meaning by itself
. Data Quality: Data that is accurate, complete, consistent, and relevant
. Accessible Data: Data for model training needs to be readily available, usable, and in the proper format
.
Layers of the Gen AI Landscape
Gen-AI-powered Application: The user-facing layer that allows users to interact with and leverage AI capabilities
. Agent: Software that learns how to best achieve a goal based on inputs and tools available to it
. Platform: Offers APIs, data management capabilities, and model deployment tools, bridging the gap between models and agents
. Model: The "brains" of the AI system; complex algorithms trained on vast amounts of data to learn patterns
. Infrastructure: Provides core computing resources (servers, GPUs, TPUs) and software to store and run AI models
.
Google Foundation Models
Gemini: Supports multimodal understanding, advanced conversational AI, content creation, and question answering
. Gemma: Offers developers user-friendly, customizable solutions for local deployments and specialized AI applications
. Imagen: A text-to-image diffusion model that generates high-quality images from textual descriptions
. Veo: Generates video content based on text descriptions or still images
.
2. Google Cloud’s Generative AI Offerings (~35%)
Understand how Google Cloud products enable generative AI business value.
Platform Strengths and Strategy
Google Cloud Strategy: An AI-first company providing an enterprise-grade foundation, open approach, and ecosystem integration
.
Gemini Products and Fit
Gemini App: A generative AI chatbot for tasks like writing, summarizing, translating, and creating images
. Gemini Advanced: Provides access to extra features and enterprise-grade protections
. Gemini for Google Workspace: Integrates Gen AI into apps like Gmail, Slides, and Meet to compose emails, generate images, and summarize notes
. Gemini for Google Cloud: An AI assistant that helps write/debug code, manage cloud applications, analyze BigQuery data, and strengthen security
. NotebookLM: Acts as a research assistant by allowing you to upload files; it summarizes points and answers questions while staying grounded in source material
. Gemini Enterprise: Integrates customized search and conversation agents accessing internal sources into an organization's internal websites or dashboards
.
Vertex AI Platform
Vertex AI: Google Cloud’s unified ML platform to build, train, and deploy ML applications
. Model Garden: Allows you to pick from existing Google, third-party, or open-source models
. Vertex AI Search: Search and recommendation solutions for your business
. Vertex AI Studio: Used for building and deploying production-ready AI applications at scale
. Google AI Studio: Free of charge tool meant for quick AI prototyping
.
Customer Engagement Suite
Conversational Agents: Act as effective chatbots for customers
. Agent Assist: Supports live human contact center agents
. Conversational Insights: Gains insights into all communications with customers
. Cloud Contact Center as a Service (CCaaS): An enterprise-grade, cloud-native contact center solution
.
Agent Tooling Concepts
Agent: An application that tries to achieve a goal by observing the world and acting upon it using tools
. Reasoning Loop: An iterative process where the agent observes, interprets, reasons, and acts
. Tools: Functionalities allowing the agent to interact with its environment (e.g., accessing data)
. Extensions: Connect agents to external services via APIs
. Functions: Define specific actions or tasks
. Data Stores: Provide access to information
. Plugins: Add new skills and integrations
.
Other Google Cloud APIs
Document AI API: Extracts data from varied formats and automates document processing
. Cloud Vision API: Analyzes image content, tags objects/text, and identifies faces
. Translation API: Translates text, documents, websites, and audio/video files
.
3. Techniques to Improve Generative AI Model Output (~20%)
Focus on how to optimize quality and reliability of generated results.
Common Gen AI Challenges
Hallucinations: When AI models produce outputs that aren't accurate or based on real information
. Bias: LLMs may learn and magnify subtle biases found in large datasets
. Fairness: Assessing fairness is a key aspect of responsible development
. Data Dependency: Model performance relies heavily on data; incomplete or biased data affects output
.
Techniques to Improve Reliability
Prompt Engineering: Creating effective inputs to guide the model
. Grounding: Connecting the AI's output to verifiable sources of information
. RAG (Retrieval-Augmented Generation): The LLM retrieves relevant information from external sources using tooling and incorporates it into the prompt
. Fine-tuning: Enhancing a pre-trained model's performance for specific tasks or domains
. Human-in-the-loop (HITL): Integrating human input and feedback directly into ML workflows to ensure accuracy and moderate content
.
Model Controls
Temperature: Controls the "creativity" or randomness of the model's word choices
. Top-p (Nucleus Sampling): Controls randomness by considering the cumulative probability of the most likely tokens
. Token Count: Represents meaningful chunks of text (words/punctuation) to limit output
. Output Length: Determines the maximum length of generated text
. Safety Settings: Filters out potentially harmful or inappropriate content
.
Prompt Engineering Techniques
Zero-shot: Asking the model to complete a task with no prior examples
. One-shot: Providing the model with one example to learn from
. Few-shot: Giving the model multiple examples to learn from
. Role Prompting: Assigning a persona to the model to influence style, tone, and focus
. Prompt Chaining: Engaging in a back-and-forth conversation or using outputs as inputs for subsequent prompts
. Chain-of-Thought (CoT): Guiding an LLM through a problem-solving process by providing examples with intermediate reasoning steps
.
Monitoring and Evaluation
Versioning: Keeping track of different model versions (e.g., with Vertex AI Model Registry)
. Performance Tracking: Reviewing metrics to check model performance
. Drift Monitoring: Watching for changes in model accuracy over time
.
4. Business Strategies for a Successful Gen AI Solution (~15%)
Connect AI technology to real business outcomes.
Business Alignment
Gen AI Strategy: Should combine a top-down approach (leadership vision) with a bottom-up approach (employee experimentation)
. Strategic Focus: Prioritize implementations with clear business value
.
Security and Responsible AI
Responsible AI: Ensuring AI applications don't cause harm and are used ethically throughout the entire lifecycle
. Secure AI Framework (SAIF): A framework to help organizations manage AI/ML model risks and ensure security
. Secure-by-Design Infrastructure: Google Cloud tools like Identity and Access Management (IAM) and Security Command Center help protect data and models
Final Thoughts
This certification is not about coding—it’s about understanding
concepts, platforms, and business impact. If you:
- Follow the official learning path
- Understand GenAI fundamentals
- Can map business needs to Google Cloud solutions
…you should be well prepared to pass the exam confidently.
Good luck, and happy learning! 🚀
**part of this post was written using AI, afterall its related to GenAI 😉
