Google Cloud Generative AI Leader Exam Preparation
Prepares you for exam : Google Cloud Generative AI Leader
Prepare for the Google Cloud Generative AI Leader exam: generative versus traditional AI, models, training and inference, data readiness and what goes wrong.
- 19 videos
- 3.3 h of video
- VideosFree on YouTube
- Hands-on labNot for this course
- Practice simulationNot for this course
- Mock examNot for this course

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About this course
Google Cloud Generative AI Leader: Exam Preparation Course
Prepare for the Google Cloud Generative AI Leader certification with this structured, beginner-friendly exam preparation course.
This playlist covers the key concepts you need to understand generative AI, Google Cloud AI solutions, business use cases, responsible AI, AI strategy, and organizational adoption.
What Youโll Learn Generative AI fundamentals Large Language Models (LLMs) Foundation models and AI capabilities Google Cloud generative AI ecosystem Google Cloud AI and data services Generative AI business use cases AI transformation and business value Prompting and prompt engineering fundamentals Responsible AI and AI governance Security, privacy, and risk considerations Generative AI implementation strategies AI adoption and organizational readiness Exam-focused concepts and terminology Practice questions and exam preparation
The course is designed to help you build both conceptual understanding and exam readiness, with practical business scenarios and examples throughout the playlist.
๐ฏ Goal: Build the knowledge and confidence needed to prepare for the Google Cloud Generative AI Leader certification exam.
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Practice simulation coming soon
This course does not have its interactive simulation yet. Follow the videos with the Hands-On Lab guide in the meantime; the simulation will appear here when it is ready.
All 19 videos, in order
- 1
6:23Google Cloud Generative AI Leader: 18 Labs to Pass the ExamOverview - 2
9:5801. Setup and How to Study This ExamHands-on - 3
11:0402. Generative Versus Traditional AIHands-on - 4
9:4203. Models, Training and InferenceHands-on - 5
10:0504. Data Types and ReadinessHands-on - 6
9:1405. What Goes WrongHands-on - 7
9:5806. Finding and Ranking Use CasesHands-on - 8
9:5907. Six Categories, Twenty-Six ProductsHands-on - 9
10:2608. Choosing a ModelHands-on - 10
9:3709. Where Building HappensHands-on - 11
10:2110. Agents on Google CloudHands-on - 12
11:4811. What Employees Use DirectlyHands-on - 13
10:0512. Grounding Data and HardwareHands-on - 14
9:1213. Prompting TechniquesHands-on - 15
10:5014. Grounding the AnswerHands-on - 16
9:0515. Improving and Measuring OutputHands-on - 17
10:1416. Principles and PracticeHands-on - 18
11:0817. Value, KPIs and AdoptionHands-on - 19
19:4918. Capstone and Exam PreparationHands-on
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