Manish Patel

    Google Cloud Generative AI Leader Full Course 2026

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    Prompt Engineering · Online Course

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    The Google Cloud Generative AI Leader certification landscape is evolving rapidly, and this course provides a structured roadmap to navigating the sophisticated suite of tools offered by the Google Cloud Platform. By focusing on the intersection of business strategy and technical implementation, the curriculum demystifies Vertex AI, guiding learners through the practical nuances of Agent Builder, Search, and the Model Garden. Beyond basic theoretical definitions, you will explore the specific architectural differences between Gemini and PaLM, learning how to select and tune models for distinct organizational objectives. A standout feature of this course is its deep dive into advanced prompt engineering, covering vital techniques like chain-of-thought, few-shot prompting, and grounding. These skills are essential for reducing hallucinations and increasing the precision of AI outputs in enterprise environments. Additionally, the course emphasizes responsible AI through Google’s Secure AI Framework (SAIF), ensuring that future leaders are equipped to build solutions that are not only innovative but also ethically sound and compliant. This path is ideal for professionals aiming to bridge the gap between AI development and leadership, providing the necessary theoretical foundation and practical question-solving strategies to excel in the certification exam while building a robust knowledge base for real-world cloud AI projects.

    About the creator

    Manish Patel

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    Manish Patel
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    Manish Patel is an expert educator in cloud computing and artificial intelligence, specifically focusing on Google Cloud Platform (GCP) and Generative AI technologies. He bridges the gap between complex engineering concepts and practical business applications. His teaching philosophy centers on rapid, comprehensive skill acquisition—taking absolute beginners to expert levels in condensed timeframes. Through hands-on demonstrations, architectural breakdowns, and real-world scenario analysis, Manish helps technical professionals, business leaders, and aspiring developers navigate the shifting landscapes of Generative AI. By structuring his courses to meet the fast-evolving demands of the 2026 tech ecosystem, Manish ensures that his students learn not just the…Show more

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    Program Overview

    Online Course

    Learning format

    Prompt Engineering

    Subcategory

    $19.99 (list $9.99)

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Understand the core concepts of Generative AI, large language models (LLMs), and their real-world applications
    • Learn how to use Vertex AI, including Vertex AI Studio, Agent Builder, Search, and Model Garden
    • Explore Google’s foundation models such as PaLM and Gemini, and how to choose the right model for your use case
    • Master prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and grounding strategies
    • Identify and evaluate GenAI use cases for business, such as content generation, summarization, classification, and personalization
    • Apply ethical and responsible AI practices based on Google’s Secure AI Framework (SAIF)
    • Design and plan GenAI-powered solutions using Google Cloud tools
    • Get familiar with Google Cloud’s GenAI Leader certification exam format, question styles, and key focus areas
    • Practice with realistic sample questions and quizzes aligned with the official exam guide
    • Gain confidence to pass the Generative AI Leader certification exam on your first attempt

    Best For

    • Aspiring AI business leaders and product managers
    • Cloud practitioners looking to validate their GenAI knowledge
    • IT professionals transitioning into Google Cloud AI roles
    • Developers needing a strategic overview of Vertex AI capabilities

    Not For

    • Data scientists seeking deep-dive code-heavy model architecture
    • Individuals looking for hands-on Python machine learning programming
    • Learners who prefer theoretical research over enterprise cloud tool application

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