Google Cloud
Google Cloud Generative AI Leader
Business-level preparation focused on Google Cloud's official Generative AI Leader blueprint.
Exam Blueprint
Fundamentals of generative AI
Core concepts, data implications, landscape layers, and Google's foundation-model portfolio.
Techniques to improve output
Prompting, grounding, RAG, mitigation of model limitations, monitoring, and sampling controls.
Google Cloud's generative AI offerings
Enterprise strengths, prebuilt offerings, customer experience products, developer tools, and agent t...
Business strategies for successful gen AI solutions
Implementation strategy, secure AI, SAIF, responsible AI, privacy, bias, and accountability.
Question Bank
82
Recall
148
Application
70
Analysis
What You'll Study
Fundamentals of generative AI
- 1.1Core generative AI concepts and use cases
- 1.2Data types and business implications
- 1.3Core layers of the gen AI landscape
- 1.4Google foundation-model strengths
Techniques to improve output
- 3.1Overcome model limitations
- 3.2Prompt engineering techniques
- 3.3Grounding techniques and model controls
Google Cloud's generative AI offerings
- 2.1Google Cloud's strengths in gen AI
- 2.2Prebuilt gen AI offerings for AI-powered work
- 2.3Offerings that improve customer experience
- 2.4Offerings that help developers build with AI
- 2.5Purpose and types of tooling for agents
Business strategies for successful gen AI solutions
- 4.1Implement a successful gen AI solution
- 4.2Secure AI and SAIF
- 4.3Responsible AI in business
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