GCP-GAI

Google Cloud Generative AI Leader (GCP-GAI)

The Introduction to Generative AI certification validates your foundational understanding of generative AI concepts, large language models, responsible AI principles, and how Google Cloud tools enable generative AI applications. This foundational-level certification is designed for business leaders, decision-makers, and professionals who want to understand how generative AI works, its potential business applications, and the Google Cloud services that support AI initiatives. The exam tests your knowledge of core AI and ML concepts, the differences between traditional ML and generative AI, how large language models like PaLM and Gemini work, prompt engineering techniques, and Google Cloud's AI platform (Vertex AI). A strong emphasis is placed on responsible AI practices and ethical considerations surrounding generative AI deployment.

Exam Details

Domain Weights

Study Tips

• Understand the difference between discriminative and generative AI models; discriminative models classify or predict, while generative models create new content
• Know the transformer architecture at a conceptual level; understand self-attention, tokens, and why transformers revolutionized NLP
• Study Google's seven AI principles thoroughly; expect questions about responsible AI practices and how to identify and mitigate bias
• Practice prompt engineering concepts; know the difference between zero-shot, few-shot, and chain-of-thought prompting
• Familiarize yourself with Vertex AI features including Model Garden, Generative AI Studio, and Vertex AI Search and Conversation
• Understand when to use fine-tuning versus prompt engineering versus retrieval-augmented generation (RAG)

AI, ML, and Deep Learning Hierarchy

Types of Machine Learning

Discriminative vs Generative Models

Exam Tip: The distinction between discriminative and generative models is fundamental. Discriminative = classifies existing data. Generative = creates new data. This is one of the most commonly tested concepts.

Key AI/ML Terminology

• Neural Network: Computing system inspired by biological neurons; consists of layers of interconnected nodes that process information; input layer, hidden layers, and output layer
• Training: Process of feeding data through a model and adjusting weights to minimize error; requires labeled data for supervised learning; measured by loss function
• Inference: Using a trained model to make predictions on new, unseen data; the production phase where the model provides value
• Parameters: Internal model variables learned during training (weights and biases); LLMs have billions of parameters (PaLM 2 has 340B, Gemini Ultra has more)
• Overfitting: Model performs well on training data but poorly on new data; memorizes noise rather than learning patterns; mitigated with regularization, dropout, and more data
• Hallucination: When a generative model produces plausible-sounding but factually incorrect or fabricated information; a key challenge in deploying LLMs

Transformer Architecture

The transformer is the foundational architecture behind modern large language models. Introduced in the 2017 paper "Attention Is All You Need," transformers replaced recurrent neural networks (RNNs) with a self-attention mechanism that processes all tokens in parallel rather than sequentially. This enables transformers to understand long-range dependencies in text and train much faster on large datasets.

Google's Foundation Models

Types of Generative AI Models

LLM Key Concepts

• Context Window: Maximum number of tokens a model can process in a single request (input + output); Gemini 1.5 Pro supports up to 1 million tokens; longer context enables processing entire documents
• Temperature: Controls randomness of output; 0 = deterministic (most likely token), 1 = highly creative/random; lower for factual tasks, higher for creative tasks
• Top-K and Top-P: Sampling strategies that limit which tokens the model considers; Top-K selects from K most likely tokens; Top-P (nucleus sampling) selects from tokens whose cumulative probability reaches P
• Embeddings: Dense vector representations of text that capture semantic meaning; similar concepts have similar embeddings; used for search, clustering, and recommendations
• Foundation Model: Large model trained on broad data that can be adapted to many tasks; forms the base for specialized applications through fine-tuning or prompt engineering

Prompt Engineering Techniques

Prompt Design Best Practices

• Be Specific: Clearly state what you want the model to do, the desired output format, length, and style; vague prompts lead to vague outputs
• Provide Context: Include relevant background information, constraints, and the target audience; the model cannot read your mind about unstated requirements
• Use Delimiters: Separate instructions from content using triple backticks, XML tags, or other markers; prevents prompt injection and improves clarity
• Iterate and Refine: Prompt engineering is iterative; start simple, evaluate the output, and incrementally add specificity until results match expectations
• Control Output: Use temperature, top-K, and top-P parameters alongside prompt design; lower temperature for factual accuracy, higher for creativity

Model Customization Approaches

Exam Tip: Know the customization spectrum from least effort (prompt engineering) to most effort (RLHF). RAG is especially important because it reduces hallucinations by grounding responses in real data without retraining.

Retrieval-Augmented Generation (RAG) in Detail

• How It Works: User query is converted to an embedding, which is used to search a vector database of document embeddings; top relevant documents are retrieved and injected into the LLM prompt as context
• Benefits: Reduces hallucinations by grounding responses in actual data; keeps information current without retraining; enables attribution and source citing
• Google Cloud Tools: Vertex AI Search provides turnkey RAG capabilities; Vertex AI Vector Search for custom embedding search; Vertex AI Extensions for connecting to external data sources
• When to Choose RAG: When you need responses based on your proprietary data, when information changes frequently, when accuracy and source attribution are critical, and when you want to avoid the cost of fine-tuning

Vertex AI Platform

Generative AI APIs

Duet AI and AI Assistants

Pre-trained AI APIs (No ML Expertise Needed)

• Vision AI: Image classification, object detection, OCR, face detection, explicit content detection, product search; pre-trained on Google's massive image dataset
• Natural Language AI: Sentiment analysis, entity recognition, syntax analysis, content classification, entity sentiment; analyze text without building custom models
• Speech-to-Text: Convert audio to text with high accuracy; supports 125+ languages; real-time streaming and batch transcription; speaker diarization
• Text-to-Speech: Convert text to natural-sounding speech; 220+ voices in 40+ languages; WaveNet and Neural2 voices for human-like quality; SSML support for fine control
• Translation AI: Translate text between 100+ languages; AutoML Translation for domain-specific models; Adaptive Translation for enterprise customization
• Document AI: Extract structured data from documents (invoices, receipts, forms, contracts); specialized processors for common document types; custom processor training

Google's Seven AI Principles

Applications Google Will NOT Pursue

• Weapons: Technologies whose purpose is to cause harm or injury to people, including weapons or surveillance that violates internationally accepted norms
• Surveillance: Technologies that gather or use information for surveillance violating internationally accepted norms
• Harm to People: Technologies whose purpose contravenes widely accepted principles of international law and human rights
• Against Norms: Technologies that cause or are likely to cause overall harm, where the benefits do not substantially outweigh the risks

AI Challenges and Risks

Responsible AI in Practice on Google Cloud

• Model Cards: Documentation describing a model's intended use, limitations, evaluation metrics, and ethical considerations; published for Google's foundation models; promotes transparency
• SynthID: Google DeepMind tool that embeds imperceptible digital watermarks into AI-generated images and text; enables detection of AI-generated content; does not affect image quality
• Safety Filters: Built-in content filters in Vertex AI that block harmful, inappropriate, or policy-violating content; configurable sensitivity thresholds across categories (hate, danger, harassment, sexual)
• Vertex AI Model Evaluation: Tools for evaluating model performance, fairness, and safety metrics; compare models across multiple dimensions before deployment; automated evaluation pipelines
• Explainability: Vertex Explainable AI helps understand why models make specific predictions; feature attribution methods; builds trust and enables debugging of model behavior

Exam Tip: Google's seven AI principles are heavily tested. Memorize all seven and understand how they apply to real-world scenarios. Also know what applications Google will NOT pursue with AI.