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Generative-AI-Leader Reliable Dumps and Google Exam Generative-AI-Leader Vce Format: Google Cloud Certified - Generative AI Leader Exam Pass for Sure
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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
- 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
- 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 4
- 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.
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Exam Generative-AI-Leader Vce Format | Dumps Generative-AI-Leader Discount
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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q19-Q24):
NEW QUESTION # 19
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. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
- B. Google Cloud relies on Vertex AI to connect to external data.
- C. The Gemini app is the primary Google Cloud tool for directly collecting data.
- D. Google Cloud's strengths are in the data analysis tools such as BigQuery.
Answer: A
Explanation:
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.
________________________________________
NEW QUESTION # 20
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
- A. Adjust the temperature parameter.
- B. Use role prompting.
- C. Use prompt chaining.
- D. Use grounding.
Answer: D
Explanation:
Grounding is the technique of "grounding" the LLM's responses in specific, authoritative data sources (like the company's official documentation). This prevents the model from "hallucinating" or providing information outside of the approved knowledge base, ensuring accuracy and relevance to the company's specific products and services.
________________________________________
NEW QUESTION # 21
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 AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
- B. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
- C. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
- D. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
Answer: B
Explanation:
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.
________________________________________
NEW QUESTION # 22
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
- A. Use prompt chaining.
- B. Use role prompting.
- C. Use few-shot prompting.
- D. Use zero-shot prompting.
Answer: A
Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to "remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
________________________________________
NEW QUESTION # 23
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents spend too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support.
What Google Cloud solution should they use?
- A. Vertex AI Conversation
- B. Pre-built RAG with Vertex AI Search
- C. Vertex AI Natural Language API
- D. Vertex AI Model Garden
Answer: B
Explanation:
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use thisindexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
________________________________________
NEW QUESTION # 24
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