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Google Generative-AI-Leader Exam : Google Cloud Certified - Generative AI Leader Exam

Generative-AI-Leader actual test
  • Exam Code: Generative-AI-Leader
  • Exam Name: Google Cloud Certified - Generative AI Leader Exam
  • Updated: Oct 22, 2026
  • Q & A: 117 Questions and Answers
  • PDF Demo
  • PC Test Engine
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  • Total Price: $59.99  

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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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.

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

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Google Cloud Certified - Generative AI Leader Sample Questions:

Question #1

According to Google-recommended practices, when should generative AI be used to automate tasks?

  • A. When tasks are repetitive and rule-based.
  • B. When tasks are highly creative and require original thought.
  • C. When tasks are complex and require strategic decision-making.
  • D. When tasks involve sensitive information or require human oversight
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

A research institution requires significant computational power and the ability to handle massive datasets efficiently. They are evaluating different cloud providers based on their infrastructure capabilities. What is a key benefit of using Google Cloud's AI-optimized infrastructure for this demanding workload?

  • A. Google Cloud's infrastructure primarily focuses on providing general-purpose virtual machines.
  • B. Google Cloud's AI infrastructure automatically over-provisions resources by a factor of ten for every AI project, ensuring zero performance bottlenecks, regardless of demand.
  • C. Google Cloud's AI infrastructure offers unlimited free access to the most advanced quantum computing resources, providing unparalleled processing power for AI tasks.
  • D. Google Cloud's AI-optimized infrastructure, including custom-designed TPUs and GPUs within hypercomputers, provides enhanced performance and scalability.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

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

Correct Answer: D  🗳️

Question #4

A market research analyst needs a Google Cloud prebuilt generative AI tool to consistently generate weekly reports summarizing key trends and news from publicly available data sources in the technology industry. They want the most efficient process, a consistent report each week covering the latest developments, and to avoid repeatedly specifying the desired industry and types of information to track. What should they do?

  • A. Use NotebookLM to analyze past reports for themes and improvements.
  • B. Use custom Gems in Gemini Advanced to save industry, trend/news types, and report formatting.
  • C. Use the Gemini app to draft each weekly industry trend report from scratch with manual input.
  • D. Use Google Workspace Gemini in Docs for collaborative report building.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?

  • A. Google Cloud Storage
  • B. BigQuery
  • C. Google Kubernetes Engine (GKE)
  • D. Vertex AI
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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