Google Cloud GenAI Leader Certification: Complete Training

Discover Google's vision for generative AI, emphasizing responsible AI, scalability, security, and collaboration, with Vertex AI, Gemini, and Google Workspace integration that enable leaders to deploy AI quickly via APIs.

Discover Google's complete generative AI ecosystem, from Gemini and Imogen to Vertex AI and Workspace integration, enabling you to build, deploy, and run AI-powered apps with security and privacy compliance.

Compare Google AI Studio with Vertex AI to explore quick prototyping and prompt testing versus full model deployment, lifecycle, customization, and enterprise integration with Google apps.

Explore the Google AI Studio console, using the free version to test models and agents, create apps, run prompts, and understand billing.

Explore Google's pre-built generative AI apps, ready to use without training or infra. Gemini, Gemini Advanced (GEMS), agent space, and notebook LM enable content generation, automation, and data-context insights.

Explore google gemini and learn to create, manage, and customize gems with prompts, default models, and knowledge bases. See use cases like vpn and mic troubleshooting for day-to-day IT tasks.

Explore how Vertex AI Search, conversational agents, Agent Assist, and Contact Center AI transform customer experience by delivering faster, context-based results and personalized support through AI–human collaboration.

Assess use case, budget, timelines, and business goals to choose between prebuilt and custom models. Start with prebuilt, then customize for domain accuracy, control, and competitive advantage as needed.

Learn how to map use cases to Google tools like Gemini, Vertex AI, AutoML, and vision and video APIs to select the right tool for each scenario.

Learn to craft precise prompts that guide AI models, boost output quality, and apply prompt engineering to elicit accurate, useful, and creative results from generative AI.

Master writing effective prompts by defining clear goals, providing context, asking direct questions, using simple language, and breaking complex queries into steps, while limiting scope to guide GenAI.

Explore first-party grounding with internal organization data, third-party grounding via external APIs, and Google search grounding for live web information to produce accurate, up-to-date responses.

Demonstrates using Google search as a grounding source to retrieve real-time information and provide cited sources, contrasting live results with outputs produced without grounding.

Measure AI performance using KPIs and metrics, detect drift, and monitor versioning and production behavior to ensure accuracy and reliability over time.

Create a clear ai adoption roadmap to coordinate across departments and reduce risk. Align three phases—assessment and planning, pilot projects, and scaling—with measurable goals and active leadership championing ai.

Leaders should treat generative AI adoption as a flexible, ongoing journey—start small, test tools, and build privacy, copyright, and safety policies while designing tool-agnostic systems.

Define and measure AI metrics to assess success, covering performance, adoption, and business impact; set clear thresholds, monitor regularly, adjust to sustain momentum and celebrate milestones.

Create a clear vision for AI adoption, communicate benefits, provide training and ongoing support, and address resistance through leadership to sustain successful AI-enabled transformation.

Lead by aligning business goals with AI capabilities, train people to use AI, uphold ethical, data-safe practices, and encourage learning and experimentation to create value through people, processes, and technology.

Develop ai talent by combining technical skills like data analysis and modeling with governance, ethics, and cross-functional collaboration, while offering training and real projects to sustain talent.

Apply data privacy and security best practices for generative ai by following rules like GDPR, planning data collection and retention, ensuring transparency, and conducting a privacy impact assessment before adoption.

Explain what bias in AI is, identify its sources in generative AI—training data, algorithm bias, and predictive bias—and illustrate with examples like face recognition and loan approvals.

Identify bias in generative ai, understand why it matters, and learn to prevent bias by reviewing training data and algorithm design for a fair, non-discriminatory ai system.

Implement governance frameworks for responsible AI development by creating clear rules, ensuring fairness and transparency, consulting domain experts, involving stakeholders, and training employees to comply with laws such as GDPR.

Define guardrails as boundaries for AI agents—permissions, rules, policies, and action limits—to keep behavior safe, with human oversight and human-in-the-loop guidance for high-risk outcomes.

Lead with human-centric values in generative AI by prioritizing transparency and accountability. Explain how the AI works, respect human needs, enhance well-being, and follow a governance framework for responsible AI.

Celebrate completing this course and prepare for the exam by emphasizing business value, responsible generative AI, and practical use cases across Vertex AI, Gemini, and RAG.