Generative AI Leader Exam
Standard exam with 50 to 60 multiple-choice questions.
- Type
- Written
- Delivery
- Both
- Duration
- 90 min
Exam sections
Fundamentals of gen AI
This section covers foundational knowledge of generative AI concepts, including NLP, ML, and foundation models. It explores data considerations such as quality, accessibility, and the implications of structured versus unstructured data, alongside Google’s core foundation model offerings like Gemini and Gemma.
Preparation tips
Focus on defining the differences between foundation and multimodal models, as well as identifying the appropriate ML lifecycle stages and corresponding Google Cloud tools.
Google Cloud’s gen AI offerings
Focuses on Google Cloud’s AI-first approach, detailing its enterprise-ready platform and infrastructure. Topics include pre-built services like Gemini for Google Workspace, customer experience tools like Vertex AI Search, and developer-focused resources such as Vertex AI Studio and Agent Builder.
Preparation tips
Study the specific use cases for Vertex AI Search and Agent Builder, and understand how Gemini for Google Workspace provides business value through its various integrated features.
Techniques to improve gen AI model output
Addresses methods for mitigating model limitations like hallucinations and bias. It covers essential prompt engineering styles such as zero-shot and ReAct, along with grounding techniques like Retrieval-augmented generation (RAG) and the use of sampling parameters to control model outputs.
Preparation tips
Review grounding techniques including RAG and the impact of sampling parameters like temperature and top-p on the determinism and creativity of model responses.
Business strategies for a successful gen AI solution
This domain outlines implementation roadmaps and the selection of appropriate AI solutions based on business requirements. It emphasizes critical security frameworks like SAIF and responsible AI principles, focusing on transparency, privacy, and accountability in organizational AI deployment.
Preparation tips
Understand the purpose and benefits of the Google Secure AI Framework (SAIF) and the importance of responsible AI principles like transparency, privacy, and accountability.