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The official Google Cloud Certified: Generative AI Leader syllabus, organized by domain. Check off each point as you review it.
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Describe core generative AI (gen AI) concepts and use cases
AI is the broad field; machine learning learns patterns from data and NLP lets computers understand and generate language. Generative AI uses foundation models, pretrained on huge datasets and adaptable to many tasks: LLMs focus on text, multimodal models combine text, images, audio and video, and diffusion models create images by removing noise. Prompt engineering steers outputs through the prompt; prompt tuning learns a small prefix.
Supervised learning trains on labeled data to classify or predict values, such as the risk of a loan default. Unsupervised learning discovers patterns in unlabeled data, for example to segment customers. Reinforcement learning learns by trial and error from rewards and penalties, like a robot learning to grasp objects. Semi-supervised learning combines a little labeled data with a lot of unlabeled data.
Supervised vs. unsupervised learning ↗What is reinforcement learning (RL)? ↗
The ML lifecycle runs from data ingestion (Pub/Sub for event streams) to data preparation (BigQuery, Dataflow), model training (code-free AutoML or custom training on Gemini Enterprise Agent Platform), deployment to endpoints for online or batch inference, and model management: Model Registry for versions, Model Monitoring for drift and Pipelines to automate the workflow.
Best practices for implementing machine learning on Google Cloud ↗MLOps on Gemini Enterprise Agent Platform ↗
Choosing a foundation model starts from the use case: modality (text, image, video or audio), the context window needed, quality, latency and cost, security and compliance, and whether it can be tuned with your own data. A bigger model isn't always better; Model Garden brings together Google, partner and open models so they can be compared.
The Prompt: What AI model is it anyway? ↗Model Garden on Gemini Enterprise Agent Platform ↗
Gen AI excels at creating content (text, images, video and code), summarizing documents and feedback, discovering information through conversational search and automating repetitive tasks. Forecasting or classification on structured data is usually a job for traditional AI, and the two combine well: traditional AI segments customers and gen AI creates personalized content for each segment.
When to use generative AI or traditional AI ↗Generative AI on Google Cloud ↗
Describe how various data types are used in gen AI and the business implications
AI depends on data quality: data must be complete, consistent across systems, valid, fresh and relevant to real deployment conditions, or bias and hallucinations follow. It must also be accessible: silos and scattered formats stall projects, and Google recommends a unified, governed platform rather than copying everything to one place, which is often costly and impractical.
Auto data quality overview ↗Your data is the fuel for AI: 5 steps to build strong data foundations ↗
Structured data follows a fixed schema of rows and columns and is queried with SQL, such as inventory or financial records. Unstructured data has no predefined model: emails, PDFs, images, audio, video or satellite imagery, and it makes up most of a company's data. JSON and XML are semi-structured. Google recommends BigQuery for structured data and Cloud Storage for unstructured data.
What is unstructured data? ↗Best practices for implementing machine learning on Google Cloud ↗
Labeled data pairs each example with its correct answer (a category, a sentiment or a value) and underpins supervised learning and supervised tuning; unlabeled data has no such answer and feeds unsupervised learning and the self-supervised pre-training of foundation models. Labeling is costly, so teams combine people and automation, with clear guidelines and quality control.
Identify the core layers of the gen AI landscape and the business implications
The infrastructure layer supplies the compute, storage and networking used to train and serve models. On Google Cloud it is grouped under AI Hypercomputer, which integrates accelerators (NVIDIA GPUs and Google-designed TPUs), open software and flexible consumption options, such as Spot VMs or reservations, to maximize goodput and keep costs under control.
The models layer is made up of foundation models: large models pretrained on massive data that can be adapted to many tasks. LLMs are one type of foundation model; others work with images, audio or video. Models differ in size, modality and cost, so choosing one is a per-use-case trade-off, and adapting a pretrained model is usually faster and cheaper than training from scratch.
What are foundation models? ↗Generative AI beginner's guide ↗
The platforms layer connects models, data and agents and hides the complexity of infrastructure. On Google Cloud it is Gemini Enterprise Agent Platform, the evolution of Vertex AI, organized into four pillars: build (Agent Studio, Agent Development Kit, Model Garden), scale (Agent Runtime), govern (Agent Registry, Agent Identity, Agent Gateway) and optimize (evaluation, simulation and observability).
An AI agent pursues a goal autonomously: it uses a model as its reasoning engine, plans, remembers and acts on external systems through tools. Unlike an assistant, it does not wait for the user to decide every step, and unlike a bot, it does not follow fixed rules. Google notes that deterministic tasks such as summarizing or classifying do not need an agentic workflow.
What are AI agents? Definition, examples, and types ↗Choose your agentic AI architecture components ↗
The applications layer is the part people interact with. It includes AI built into everyday tools, such as Gemini in Gmail or Docs, and the Gemini Enterprise app, a single front door to search company data with permissions, chat and use agents. It delivers value quickly without in-house development, and Google states that company data is not used to train its models.
What is Gemini Enterprise? ↗AI Tools for Business | Google Workspace ↗
Identify the use cases and strengths of Google’s foundation models
Gemini is Google's family of most capable multimodal models, built by Google DeepMind: it understands text, images, audio, video and code in a single request and reasons across them. Its long context window lets it analyze large volumes of documents or video without splitting them, and it is the reasoning engine behind assistants and agents such as Gemini Enterprise. It is used as a managed service on Agent Platform.
Gemma is a family of lightweight open models built from the same research as Gemini. Because the weights are open, it can be tuned with any framework and run on your own hardware, mobile devices or hosted services. It has specialized variants such as MedGemma (medical), ShieldGemma 2 (safety) and EmbeddingGemma (search), and Google recommends Agent Platform to serve it without an MLOps team.
Imagen is Google's specialized text-to-image model. Its strengths are image quality, photorealism, artistic styles, typography and brand, logo and product designs, with low latency; it also supports mask-based editing and upscaling. For multi-turn conversational editing Google recommends Gemini instead, and content generated with Imagen carries the invisible SynthID watermark.
Generate and edit images on Vertex AI ↗Announcing Veo 3, Imagen 4, and Lyria 2 on Vertex AI ↗
Veo is Google DeepMind's model for generating video from text or images, across a wide range of visual and cinematic styles. It can animate existing photos, create the footage between a first and a last frame, extend and edit clips and generate native audio with dialogue and sound effects. Companies use it to speed up ads and marketing content, and its videos carry the SynthID watermark.
Generate videos with Agent Platform ↗Veo 3 Fast available for everyone on Vertex AI ↗