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

Generative-AI-Leader real exams

Exam Code: Generative-AI-Leader

Exam Name: Google Cloud Certified - Generative AI Leader Exam

Updated: Nov 06, 2026

Q & A: 117 Questions and Answers

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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
  • 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
  • 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 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.

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

Google Cloud Certified - Generative AI Leader Sample Questions:

Question #1
A national bank is overwhelmed by customer inquiries across multiple channels and needs an AI-powered solution to provide seamless, consistent support, empower customer support agents, and improve service quality. What Google Cloud product should the bank use?

A. Gemini for Google Workspace
B. Vertex AI Search
C. Gemini for Google Cloud
D. Google Contact Center as a Service


Question #2
A global news agency is developing a generative AI tool to quickly summarize breaking news articles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?

A. Vertex AI Natural Language API
B. Document AI
C. BigQuery
D. Grounding with Google Search


Question #3
What is the function of the platform layer in the generative AI (gen AI) landscape?

A. To provide tools for users to interact with gen AI models, and build and deploy AI solutions.
B. To allow access to pre-trained gen AI models.
C. To allow users to interact with and leverage AI capabilities.
D. To provide the computational resources needed to train and run generative AI models.


Question #4
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 involve sensitive information or require human oversight
D. When tasks are complex and require strategic decision-making.


Question #5
A software engineering team is experimenting with generative AI within their coding process. What is a benefit of using gen AI to create unit tests for code?

A. Reducing code complexity.
B. Guaranteeing errorless code.
C. Reducing manual validation effort.
D. Eliminating the need for human intervention.


Solutions:

Question #1
Correct Answer: D
Question #2
Correct Answer: D
Question #3
Correct Answer: A
Question #4
Correct Answer: A
Question #5
Correct Answer: C

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