Reliable and Accurate Google Generative-AI-Leader Exam Questions

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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
What is a primary benefit of using a multi-agent system?

A. To serve as a platform for hosting traditional, non-AI applications.
B. To simplify the most basic and repetitive rule-based tasks.
C. To consolidate all unique AI functions into a single, undifferentiated model.
D. To manage complex tasks that demand coordinated AI functions.

Answer: D

Explanation:
Multi-agent systems are designed to tackle complex problems by breaking them down into sub-tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.
________________________________________

NEW QUESTION # 37
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

A. Data agent
B. Customer service agent
C. Security agent
D. Code agent

Answer: C

Explanation:
Given the tasks

NEW QUESTION # 38
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?

A. To analyze situations, use multiple tools, and make informed decisions without requiring constant human input.
B. To store and manage large datasets used for training and running gen AI models.
C. To provide the computational resources needed to train and run gen AI models.
D. To provide a user-friendly interface for interacting with gen AI models.

Answer: A

Explanation:
An AI agent, especially in the context of generative AI, is designed to be more autonomous and capable than a simple model. Its function is to understand a goal, analyze a situation, leverage various tools (including other generative AI models or external APIs), and make decisions or take actions to achieve that goal, often with minimal human intervention.
________________________________________

NEW QUESTION # 39
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?

A. More granular control over support team access and permissions for sensitive data.
B. Improved productivity and data interaction using AI assistants and advanced document analysis.
C. Greater interoperability with legacy software systems and databases.
D. Enhanced real-time communication and collaboration among team members.

Answer: B

Explanation:
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.

NEW QUESTION # 40
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?

A. Use gen AI to identify potential bugs and security vulnerabilities in their code.
B. Use gen AI to refactor and optimize existing code.
C. Use gen AI to automatically generate comprehensive documentation for their code.
D. Use gen AI to suggest code snippets and complete functions.

Answer: D

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.
________________________________________

NEW QUESTION # 41
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Reliable and Accurate Google Generative-AI-Leader Exam Questions

Google Generative-AI-Leader PDF Questions - Increase Your Exam Passing Chances

Google Generative-AI-Leader Exam Syllabus Topics:

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q36-Q41):

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