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[General] Trustworthy Generative-AI-Leader Exam Torrent & Generative-AI-Leader Test Pd

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【General】 Trustworthy Generative-AI-Leader Exam Torrent & Generative-AI-Leader Test Pd

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Google Generative-AI-Leader Exam Syllabus Topics:
TopicDetails
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
  • 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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q41-Q46):NEW QUESTION # 41
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?
  • A. Security Command Center
  • B. Identity and Access Management
  • C. Workload monitoring tools
  • D. Secure-by-design infrastructure
Answer: B
Explanation:
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
________________________________________

NEW QUESTION # 42
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
  • A. Ensuring that the AI model is trained on a large and diverse dataset.
  • B. Applying data anonymization techniques to remove or obscure sensitive data.
  • C. Focusing on collecting only quantitative feedback data in future surveys.
  • D. Implementing strong access controls to limit which teams can view the raw survey data.
Answer: B
Explanation:
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google's Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model's parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application's outputs. This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn't solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation.
(Reference: Google Cloud's Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)

NEW QUESTION # 43
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. Dataflow
  • C. Document AI API
  • D. Natural Language API
Answer: C
Explanation:
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.
________________________________________

NEW QUESTION # 44
A retail company with a large online catalog wants to improve customer experience and drive sales by implementing multimodal search capabilities (image, voice, and text). What is a primary business benefit of this capability?
  • A. Improved customer engagement and product discovery leading to increased satisfaction and potential sales.
  • B. Reduced dependency on keyword optimization for product listings and improved search engine rankings.
  • C. Streamlined inventory management processes and more accurate demand forecasting for popular items.
  • D. Lowered operational costs associated with managing and updating product information across different platforms and channels.
Answer: A
Explanation:
Multimodal search directly enhances the customer experience by allowing them to find products using various intuitive methods (images, voice, text). This leads to easier product discovery, higher engagement, and ultimately increased customer satisfaction and potential sales, which is a primary business benefit.
________________________________________

NEW QUESTION # 45
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 provide a user-friendly interface for interacting with gen AI models.
  • C. To store and manage large datasets used for training and running gen AI models.
  • D. To provide the computational resources needed to train and run 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 # 46
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