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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
Topic 2
  • Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3
  • Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 4
  • Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?

Answer: B

Explanation:
While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping.
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NEW QUESTION # 91
A research company needs to analyze several lengthy PDF documents containing financial reports and identify key performance indicators (KPIs) and their trends over the past year. They want a Google Cloud prebuilt generative AI tool that can process these documents and provide summarized insights directly from the source material with citations. What should the analyst do?

Answer: A

Explanation:
The requirements are for a prebuilt tool that is designed for:
Analyzing uploaded private documents (lengthy PDFs).
Providing summarized insights (extracting KPIs and trends).
Offering citations (grounding the answers to the source material).
NotebookLM (C) is the Google tool explicitly designed for this use case. It is a generative AI powered notebook/research assistant that allows users to upload source documents (including PDFs), then ask questions and generate summaries or insights that are grounded in and cited back to the source documents.
This makes it an ideal prebuilt solution for an analyst who needs to process complex, lengthy financial reports and verify the data with citations.
Gemini Advanced (A) and Gemini app (B) are general-purpose conversational tools that are not primarily focused on deep, grounded analysis of uploaded documents that require source citations for research integrity.
Gemini for Google Workspace (D) is limited to data already in Workspace apps (Docs, Gmail, Drive) and the manual copy/paste process would be inefficient for " several lengthy PDF documents. " (Reference: Google ' s Generative AI Leader training materials highlight NotebookLM as the specific generative AI application built for research and information synthesis from uploaded documents, offering key features like grounding and citations back to the source material.)


NEW QUESTION # 92
Pike and Rowan Law uses a generative AI system to produce first draft contract clauses for its clients. To ensure accuracy and compliance, a licensed attorney must review, edit, and approve each AI draft before any client sees it. This addition of expert oversight within the AI workflow represents which recommended practice?

Answer: D

Explanation:
The correct option is Human in the Loop (HITL) because a licensed attorney must review, edit, and approve each AI draft before any client sees it.
Adding human oversight ensures accuracy, legal compliance, and accountability for outputs. HITL is a recommended practice for high-stakes domains where expert judgment must validate model results before release, which matches a legal drafting workflow that requires professional approval.


NEW QUESTION # 93
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?

Answer: D

Explanation:
The scenario requires quick creation of a training video using a custom avatar and voiceover while adhering to zero cost for production.
Google Vids is an AI-powered video creation app (part of Google Workspace/Gemini features) designed to make video creation accessible for teams without the overhead of traditional production. It specifically offers features like AI avatars and voiceovers for content such as trainings, demos, and onboarding videos. This directly addresses the need for a low-cost, fast solution for a new hire training video with custom branding elements (custom avatars and voiceovers are a key feature of the tool).


NEW QUESTION # 94
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?

Answer: B

Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
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NEW QUESTION # 95
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