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Exam Cram Generative-AI-Leader Pdf | Pass Leader Generative-AI-Leader Dumps

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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

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

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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?

A. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
B. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
C. RAG uses human oversight to ensure accuracy before presenting information to the customer.
D. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.

Answer: A

Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
________________________________________

NEW QUESTION # 39
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

A. Gemini deployment for this scenario would be too expensive and complex.
B. Gemini cannot integrate with required financial databases.
C. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
D. Gemini is not equipped to handle structured numerical data for financial assessments.

Answer: C

Explanation:
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.
________________________________________

NEW QUESTION # 40
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. Enhanced real-time communication and collaboration among team members.
B. More granular control over support team access and permissions for sensitive data.
C. Greater interoperability with legacy software systems and databases.
D. Improved productivity and data interaction using AI assistants and advanced document analysis.

Answer: D

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 # 41
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. Security agent
B. Data agent
C. Code agent
D. Customer service agent

Answer: A

Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
________________________________________

NEW QUESTION # 42
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?

A. Vertex AI
B. Google Kubernetes Engine (GKE)
C. Google Cloud Storage
D. BigQuery

Answer: A

Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
________________________________________

NEW QUESTION # 43
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