Generative-AI-Leader Passing Score & Valid Generative-AI-Leader Exam Topics

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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
  • 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 3
  • 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 4
  • 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory data. They want the most cost-effective solution. What should the organization do?

Answer: D

Explanation:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.


NEW QUESTION # 87
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

Answer: A

Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.


NEW QUESTION # 88
A large online retailer with a vast product catalog wants to improve customer satisfaction by making it easier for shoppers to find the specific products they're looking for. The retailer also wants to provide personalized recommendations to increase sales. What should the company do?

Answer: D

Explanation:
AI Commerce Search on Gemini Enterprise for Customer Experience addresses both requirements: helping shoppers discover products through natural-language searches and delivering personalized recommendations that can increase conversions. It is purpose-built for commerce experiences and can interpret user intent, improve result relevance, and support individualized product discovery across large catalogs. Recommendations alone addresses personalization but does not fully solve the natural-language product-search requirement. Vision API can identify and label image content, but image tagging by itself does not provide a complete commerce-search and recommendation experience. Agent Search on Gemini Enterprise Agent Platform is intended primarily for enterprise employees searching internal organizational information, not customers navigating a retail catalog. Because option C combines intelligent product search, personalized recommendations, and improved discovery within a commerce-focused offering, it is the most comprehensive solution.


NEW QUESTION # 89
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: A

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 # 90
A company wants to choose a generative AI (gen AI) use case that will be successful and have the most impact. What key factor should they determine first according to Google Cloud-recommended practices?

Answer: B

Explanation:
According to Google ' s principles for successful AI adoption, organizations should adopt a " problem-first " approach to ensure their investments deliver measurable value. The strategic choice of a use case should always be motivated by a clear business imperative.
Determining the specific business problems and desired outcomes (B) is the foundational step in any successful Gen AI strategy. Without a well-defined problem (e.g., " reduce customer response time by 30% " ) and a measurable desired outcome (e.g., " increase customer satisfaction scores " ), any AI solution runs the risk of being a technology in search of a purpose, leading to limited adoption or failure to deliver meaningful ROI.
Options A, C, and D are considerations secondary to the initial strategic alignment:
Availability of models (C) only dictates the technical feasibility, not the business value.
Training employees (A) is a resource requirement, not the goal itself.
Model updates (D) is a technical concern related to model longevity, not the primary strategic driver for use case selection.
The priority is always to align the AI solution with high-value business objectives.
(Reference: Google Cloud Generative AI strategy guidelines state: " A fundamental principle for successful AI adoption, including generative AI, is to start with clear business problems and desired outcomes. Without a well-defined problem, the AI solution might not deliver meaningful value, regardless of the technology used.
This ' problem-first ' approach is crucial for impactful AI strategy. " )


NEW QUESTION # 91
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