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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
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

A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

A. Cloud Storage
B. BigQuery
C. Model Registry
D. Vertex AI Pipelines

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #2

A development team is building an internal knowledge base chatbot to answer employee questions about company policies and procedures. This information is stored across various documents in Google Cloud Storage and is updated regularly by different departments. What is the primary benefit of using Google Cloud's RAG APIs in this scenario?

A. They automatically create summaries of all company policies, which are then presented to employees as quick answers.
B. They enable the generative AI model to retrieve the most up-to-date and relevant information from the policy documents in real-time.
C. They allow the development team to train a single foundation model on all company documents.
D. They provide a pre-built user interface for the chatbot, simplifying the front-end development process.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #3

What is the definition of a foundation model?

A. A statistical language model, trained on a massive dataset, that can be used to perform sentiment analysis.
B. A large AI model trained on a massive dataset, often multimodal, that can be adapted to solve various problems.
C. A subset of artificial intelligence that uses machine learning to process and interpret text and data.
D. A type of machine learning that uses artificial neural networks to learn from data.

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Correct Answer: B 🗳️

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Question #4

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

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #5

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