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[General] Generative-AI-Leader Testengine - Generative-AI-Leader Prüfung

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【General】 Generative-AI-Leader Testengine - Generative-AI-Leader Prüfung

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Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q54-Q59):54. 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. Enhanced real-time communication and collaboration among team members.
  • B. Greater interoperability with legacy software systems and databases.
  • C. Improved productivity and data interaction using AI assistants and advanced document analysis.
  • D. More granular control over support team access and permissions for sensitive data.
Antwort: C
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.

55. Frage
What does Model Garden enable a company to do?
  • A. Train new models from scratch using large datasets.
  • B. Evaluate the performance of different models using various metrics.
  • C. Manage different versions of a model, including the code, data, and parameters used to train it.
  • D. Discover, customize, and deploy existing models from Google and its partners.
Antwort: D
Begründung:
Model Garden is a key component of the Vertex AI Platform on Google Cloud, positioned as an AI/ML model library. Its core function is to provide a central, organized place for users to find and utilize a wide variety of machine learning assets.
Specifically, Model Garden enables customers to:
Discover a curated collection of models, including Google's latest Foundation Models (like Gemini and Imagen), specialized models, and enterprise-ready models from Google partners and the open-source community (e.g., Gemma).
Test and customize these models, often with tools like Vertex AI Studio for prompt tuning or fine-tuning with custom data.
Deploy the selected and customized models directly to applications with a consistent deployment pattern.
Options B and C describe features of other MLOps tools within Vertex AI (Model Evaluation and Model Registry/Metadata Management). Option D describes the Custom Training service within Vertex AI. Model Garden's unique value proposition is acting as the starting point: a marketplace or repository to discover and immediately deploy or customize existing, pre-trained models.
(Reference: Google Cloud documentation states that Model Garden on Vertex AI is a place to discover, test, customize, and deploy a wide variety of models from Google and Google partners, including first-party and open-source models.)

56. Frage
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?
  • A. The challenge of efficiently producing high-quality written summaries and initial drafts of financial communications.
  • B. The complexity of building and deploying sophisticated internal knowledge bases to answer employees' finance-related questions with accurate and up-to-date information.
  • C. The struggle to accurately extract key financial figures and insights from a variety of document formats, such as balance sheets and income statements, for quick reporting.
  • D. The difficulty in analyzing large datasets of financial transactions and market data to identify anomalies and predict future financial performance.
Antwort: A
Begründung:
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language-based tasks, particularly the generation and summarization of text.
The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D).
Summarizing text: condensing long communications or documents (Option D).
Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
Option B (Analyzing large datasets/predicting performance) requires traditional machine learning (ML) models or analytical tools like BigQuery ML, as LLMs are not specialized for numerical predictive modeling.
Option C (Extracting key financial figures from documents) is a task for a highly specialized tool like Google's Document AI.
Option A (Building internal knowledge bases for Q&A) is a broader use case that is best solved with a platform solution using RAG, such as Vertex AI Search, not just a base model.
(Reference: Google's description of the Gemma model family emphasizes its role as a flexible, open LLM that excels at language fundamentals, making it ideal for content creation, summarization, and other text generation tasks.)

57. Frage
A home loan company is deploying a generative AI system to automate initial loan application reviews. Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?
  • A. Ensuring AI decision-making is explainable to understand decision reasons and establish accountability.
  • B. Implementing stricter data security measures to protect applicants' financial information from unauthorized access.
  • C. Increasing the speed at which the AI system processes loan applications to handle the high volume.
  • D. Regularly updating the AI model with more financial data to improve its accuracy over time.
Antwort: A
Begründung:
The problem centers on unexpected rejections and potential bias in a high-stakes, regulated domain (lending). In such a context, the central tenet of Responsible AI is transparency and fairness.
While all options are valid goals, the priority when facing bias concerns and customer complaints due to rejection is to provide accountability and verify the fairness of the automated decision. This is achieved through Explainable AI (XAI).
Ensuring AI decision-making is explainable (B) means building mechanisms that allow developers, regulators, and affected customers to understand why a specific decision (rejection) was made. Explainability is crucial for:
Auditing for bias: If the reasons for rejection can be traced (e.g., system rejects based on loan-to-value ratio, not race), bias can be identified and corrected.
Compliance: Financial services are heavily regulated, and the ability to explain a lending decision is often a legal or regulatory requirement.
Customer Trust: Providing a clear reason for rejection (even if the news is bad) reduces complaints and fosters confidence, directly addressing the core issue of unexpected rejections.
Options A, C, and D address security, speed, and accuracy, respectively, but Explainability is the direct mechanism for proving fairness and ensuring accountability, making it the most critical priority in this scenario.
(Reference: Google's Responsible AI principles and training materials highlight that in high-stakes domains like finance, explainability is essential for establishing trust, identifying and mitigating bias, and meeting regulatory compliance.)

58. Frage
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time- consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?
  • A. Vision AI
  • B. Document AI API
  • C. Dataflow
  • D. Natural Language API
Antwort: B
Begründung:
Document AI API is specifically designed for intelligent document processing. It uses machine learning to extract structured data from unstructured documents like scanned forms and PDFs, even with varying layouts.
This directly addresses the challenge of automating data extraction from loan applications. Natural Language API focuses on text understanding, Vision AI on image analysis (not structured extraction from documents), and Dataflow is for data processing pipelines.
________________________________________

59. Frage
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