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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?

A. Enhanced real-time communication and collaboration among team members.
B. Improved productivity and data interaction using AI assistants and advanced document analysis.
C. More granular control over support team access and permissions for sensitive data.
D. Greater interoperability with legacy software systems and databases.

Answer: B

Explanation:
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.

NEW QUESTION # 12
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

A. Fine-tune the gen AI model.
B. Implement explainable gen AI policies.
C. Collect a larger and more diverse dataset for the gen AI model.
D. Develop fairness assessments for the gen AI model.

Answer: B

Explanation:
The core problem is the lack of reasons for rejection, leading to customer complaints. This falls under the domain of explainable AI (XAI). Implementing explainable gen AI policies or mechanisms would allow the institution to provide transparency into how the AI made its decision, addressing the customer complaints directly. While other options might improve the model, they don't directly solve the transparency issue.
________________________________________

NEW QUESTION # 13
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. The Gemini app is the primary Google Cloud tool for directly collecting data.
B. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
C. Google Cloud's strengths are in the data analysis tools such as BigQuery.
D. Google Cloud relies on Vertex AI to connect to external data.

Answer: B

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 # 14
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?

A. Gemma
B. Gemini
C. Veo
D. Imagen

Answer: A

Explanation:
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.
________________________________________

NEW QUESTION # 15
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 Kubernetes Engine (GKE)
B. BigQuery
C. Vertex AI
D. Google Cloud Storage

Answer: C

Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
________________________________________

NEW QUESTION # 16
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Generative-AI-Leader Guaranteed Passing, Generative-AI-Leader Valid Exam Syllabus

Generative-AI-Leader Valid Exam Syllabus, Generative-AI-Leader Test Tutorials

Google Generative-AI-Leader Exam Syllabus Topics:

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q11-Q16):

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