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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?

Answer: B

Explanation:
Grounding is the technique of "grounding" the LLM's responses in specific, authoritative data sources (like the company's official documentation). This prevents the model from "hallucinating" or providing information outside of the approved knowledge base, ensuring accuracy and relevance to the company's specific products and services.


NEW QUESTION # 98
A national bank is overwhelmed by customer inquiries across multiple channels and needs an AI-powered solution to provide seamless, consistent support, empower customer support agents, and improve service quality. What Google Cloud product should the bank use?

Answer: B

Explanation:
The bank ' s requirement is for a solution that provides seamless, consistent support across multiple channels and helps to empower customer support agents and improve service quality. This describes the need for a comprehensive, end-to-end customer service infrastructure.
Google Contact Center as a Service (CCaaS) is the full, cloud-native contact center solution offered by Google Cloud (part of the Customer Engagement Suite). It is specifically designed to unify customer interactions across various channels (phone, chat, web messaging) and provides the necessary infrastructure for routing, managing agent workflows, and ensuring a consistent and secure customer experience at scale.
This solution goes beyond simply automating a chatbot.
While Vertex AI Search (A) can be used as a component within the solution to ground answers in an internal knowledge base, and Gemini for Google Workspace (B) can boost individual agent productivity, neither provides the comprehensive multi-channel contact center infrastructure that the scenario demands. The scale and nature of the problem-unifying overwhelmed support across channels and empowering agents-requires an enterprise-grade platform, which is precisely the function of Google Contact Center as a Service.


NEW QUESTION # 99
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: C

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 # 100
What is a characteristic of Google Cloud as a generative AI company?

Answer: B

Explanation:
Google Cloud emphasizes an AI-first approach, integrating AI capabilities across its services and consistently innovating with new models and features. While security is a high priority, fully autonomous AI agents requiring zero configuration are generally not the norm, and "completely secured and isolated from external networks" is an oversimplification of cloud security models. Google also contributes to and supports open- source AI initiatives, not solely relying on proprietary closed-source technologies.
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NEW QUESTION # 101
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?

Answer: A

Explanation:
The core problem is the model's hallucination-it invented a factual detail-in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval-Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).
Option B, fine-tuning, is expensive and only updates the model's general knowledge and style; it does not prevent the model from guessing or fabricating details when retrieving information. Option C, increasing temperature, would make the output less consistent and more diverse, likely increasing the chance of hallucination, which is the opposite of the desired effect. Option A is unrelated to factual accuracy. Therefore, Grounding is the necessary step to anchor the model's responses to the true content of the source articles.
(Reference: Google Cloud documentation on RAG/Grounding emphasizes that its primary purpose is to address the "knowledge cutoff" and hallucination issues of LLMs by retrieving relevant, up-to-date information from external knowledge sources and using this retrieved information to ground the LLM's generation, ensuring factual accuracy.)


NEW QUESTION # 102
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