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Free PDF Generative-AI-Leader - Trustable Google Cloud Certified - Generative AI Leader Exam Test Lab Questions

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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

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 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.

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

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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
What does Vertex AI Search enable companies to do?

A. To compare products from numerous online retailers, allowing users to find the best deals and product options across the internet.
B. To ground LLM responses with first-party data, third-party data, and Google's knowledge graph.
C. To surface the most popular and frequently accessed content based on global user search patterns and trends.
D. To index and retrieve information from the entire public web, providing a comprehensive view of publicly available data.

Answer: B

Explanation:
Vertex AI Search is designed to enable powerful search experiences over an organization's own data (first- party), external data (third-party), and can leverage Google's knowledge graph to provide more relevant and accurate responses, especially when grounding Large Language Models (LLMs). It does not index the entire public web like Google Search.
________________________________________

NEW QUESTION # 63
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?

A. Improved IT infrastructure management across offices.
B. Automation of employee performance reviews using AI.
C. Enhanced data encryption and compliance for internal communications.
D. Seamless knowledge sharing and collaboration across internal systems.

Answer: D

Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
________________________________________

NEW QUESTION # 64
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?

A. A customized learning agent
B. A learning management system (LMS)
C. A large language model fine-tuned on educational content
D. An AI-powered recommendation system for learning resources

Answer: A

Explanation:
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
________________________________________

NEW QUESTION # 65
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?

A. Deep learning
B. Unsupervised learning
C. Supervised learning
D. Reinforcement learning

Answer: B

Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify "anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
________________________________________

NEW QUESTION # 66
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

A. Use Gemini for Google Workspace to facilitate collaborative document review.
B. Use Vertex AI Agent Builder to create a custom AI agent.
C. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
D. Use Vertex AI Search to index the papers and enable keyword-based searches.

Answer: B

Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
________________________________________

NEW QUESTION # 67
......

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