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Die seit kurzem aktuellsten Google Cloud Certified - Generative AI Leader Exam Prüfungsunterlagen, 100% Garantie für Ihen Erfolg in der Google Generative-AI-Leader Prüfungen!

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Google Generative-AI-Leader Prüfungsplan:

Thema
Einzelheiten

Thema 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.

Thema 2

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.

Thema 3

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.

Thema 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.

Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q39-Q44):

39. Frage
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?

A. The Gemini app is the primary Google Cloud tool for directly collecting data.
B. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
C. Google Cloud's strengths are in the data analysis tools such as BigQuery.
D. Google Cloud relies on Vertex AI to connect to external data.

Antwort: B

Begründung:
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
________________________________________

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

Antwort: A

Begründung:
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.

41. Frage
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?

A. Veo
B. Gemini
C. Gemma
D. Imagen

Antwort: C

Begründung:
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.
________________________________________

42. Frage
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?

A. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
B. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
C. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
D. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.

Antwort: B

Begründung:
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
________________________________________

43. Frage
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?

A. Use NotebookLM to upload and analyze the documents.
B. Use the Gemini app to ask general financial trend questions.
C. Create a custom Gem in Gemini Advanced with predefined KPIs to look across different financial reports.
D. Use Gemini for Google Workspace within Google Docs to copy and paste sections of the reports for summary and analysis.

Antwort: A

Begründung:
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.)

44. Frage
......

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