Google Cloud Generative AI Leader: Labs & Exam Prep 2026

Discover natural language processing fundamentals and applications, from text preprocessing and language understanding to tasks such as sentiment analysis, named entity recognition, and information extraction.

Explore supervised learning with labeled inputs for accurate predictions in binary and multi-class classification, regression, and ensembling, and contrast unsupervised learning that clusters unlabeled data in real time.

Learn how machine learning models represent data, use training data and learning algorithms to train, apply hyper tuning, deploy a trained model, and perform inference to generate predictions.

Feature engineering cleans unstructured data, removes outliers and duplicates, and creates new features to train machine learning models.

Explore how inference operates by inputting data into a production machine learning model to request and receive a prediction, illustrated with a banana example and a confidence score of 0.9.

Explore how large language models power generative AI, how they predict next words from a trillion-word training set and context, and why bias and inaccuracies require careful, skeptical use.

Explore how tokens and embeddings power large language models by turning words into vectors, using tokenization and vector arithmetic (king minus man plus woman) to reveal semantic relationships.

Learn how to tune prompts for language models using temperature, input and output token limits, seed, and top p, balancing determinism, creativity, and response length for consistent outputs.

Explore zero shot prompting, asking a chatbot to perform tasks without examples. Rely on the model's knowledge to generate outputs with minimal effort, but note limited performance and prompt dependence.

Learn how few-shot prompting uses examples within prompts to guide llms toward desired outputs, acting as a mini tutorial and reference for task style.

Learn prompt chaining to break complex tasks into smaller steps when the context window is limited, including translating subtitles and generating grammar rules with chain-of-thought prompting.

Explore how CPUs, GPUs, TPUs, DPUs, and QPUs power gaming, graphics, AI, and data centers, with guidance on energy efficiency, scalability, and quantum computing potential.

Explore three approaches to retrieval-augmented generation on Google Cloud: Vertex AI Search and Conversation, Vertex AI Rounding, and custom RAG with embeddings and vector databases.

Explore Vertex AI search features, including custom search, site search with AI mode, media search, and commerce search, and learn grounding to real data via Google search, Maps, and rag.

Explore grounding with Google in Vertex AI Studio, enabling Google Search and Maps to retrieve up-to-date information via spatial queries and local business data, while noting structure output restrictions.

Discover grounding with a RAG engine and Vertex AI search, connect to Pinecone vector store, and implement corpus import and index creation for GCP deployment.

Explore vertex ai search, a managed vector search engine, with four modes: custom search, site search, media search, and commerce search, and ingestion from bigquery, cloud storage, and cloud sql.

Navigate model categories, search for foundation and fine-tuned models along with open source options, view model cards, and explore code generation, language models, chat, and text-to-image tasks.

Explore the palm to chat interface, test chat bison, view model details, adjust temperature and token limits, and save or test prompts within your my prompts panel.

Compare Gemma open weights with Gemini hosted models, and see how multimodal inputs like images are supported via Vertex AI and Google AI Studio.

Explore Google Cloud's agent assist, an AI copilot guiding customer service agents in real time. See how knowledge and generative assists, guided by a conversation profile, boost capacity and satisfaction.

Explore building and configuring conversational agents with Google Cloud Generative AI. Set up pre-built conversations agents, enable knowledge assist with summarization, and prototype smart reply models.

Understand how conversational agents work with prebuilt agents and predefined templates, explore default flows and intents like check order status, and see how the simulator tests interactions.

Vertex AI Studio's chat interface demonstrates setting system instructions and a Japanese instructor role with JLPT N5 scope, then comparing the Pro model's thinking with structured JSON output.

Explore Vertex AI Studio Part 2 to master structured JSON output and JSON schema, enable grounding with Google Search, manage prompts, and tune output with temperature controls.

Explore Vertex AI Studio's image and video generation, with models, aspect ratios, 1024 resolution, safe settings, inpainting, and style references to create dachshund visuals.

Explore generating music with Lyrica in Vertex AI Studio, crafting prompts for an energetic space opera anime, evaluating samples, and refining musical structure within audio length limits.