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  • Total Questions: 117
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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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
  • 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.

Reference: https://cloud.google.com/developers/generative-ai-leader

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Google Cloud Certified - Generative AI Leader Sample Questions:

A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?

  • A. Knowledge cutoff
  • B. Hallucination
  • C. Bias
  • D. Data dependency
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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A company is developing a generative AI-powered customer support chatbot. They want to ensure the chatbot can answer a wide range of customer questions accurately, even those related to recently updated product information not present in the model's original training dat a. What is a key benefit of implementing retrieval-augmented generation (RAG) in this chatbot?

  • A. RAG will enable the chatbot to fine-tune its underlying language model on the fly based on customer interactions.
  • B. RAG will enable the chatbot to access and utilize external, up-to-date knowledge sources to provide more accurate and relevant answers.
  • C. RAG will significantly reduce the computational resources required to run the generative AI model.
  • D. RAG will primarily help the chatbot generate more creative and engaging conversational responses.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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An organization wants to quickly experiment with different Gemini models and parameters for content creation without a complex setup. What service should the organization use for this initial exploration?

  • A. Gemini for Google Workspace
  • B. Vertex AI Prediction
  • C. Google AI Studio
  • D. Vertex AI Studio
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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What are core hardware components of the infrastructure layer in the generative AI landscape?

  • A. User interfaces
  • B. TPUs and GPUs
  • C. Pre-trained models
  • D. Tools and services for building AI models
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).

What is a definition of an AI agent?

  • A. A type of user interface that allows users to interact with a gen AI application.
  • B. A static component of a gen AI application that provides information to the user.
  • C. An application that learns how to achieve a goal based on inputs and tools available to it.
  • D. A human expert who oversees and manages the operation of a gen AI application.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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