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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

Answer: A

Explanation:
The objective is to make the generated summaries concise--that is, to control their length. In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is "easy to digest," as requested.


NEW QUESTION # 20
A retail company with a large online catalog wants to improve customer experience and drive sales by implementing multimodal search capabilities (image, voice, and text). What is a primary business benefit of this capability?

Answer: D

Explanation:
Multimodal search directly enhances the customer experience by allowing them to find products using various intuitive methods (images, voice, text). This leads to easier product discovery, higher engagement, and ultimately increased customer satisfaction and potential sales, which is a primary business benefit.


NEW QUESTION # 21
A company's customer service chatbot is built on limited rules and struggles with complex customer queries, which causes customer frustration. They want to try different generative AI models and modify them to improve performance, but their current AI setup makes this difficult and costly. Which Google Cloud benefit would help solve this problem?

Answer: B

Explanation:
Google Cloud provides access to multiple pre-trained foundation models that organizations can evaluate and adapt without developing a model from the beginning. Tunable models allow the company to modify model behavior using domain-specific examples so the chatbot can better address complex customer questions, follow the preferred response style, and use relevant terminology. This directly resolves the difficulty and expense of experimenting with and customizing models. Integrated MLOps tools help manage development, deployment, monitoring, and lifecycle processes, but they do not primarily supply the model choice and customization requested. Comprehensive security protects systems and data, while AI-optimized infrastructure supplies scalable computation. Both are valuable but do not directly address this scenario. Therefore, access to pre-trained and tunable models is the Google Cloud benefit most closely aligned with the company's requirements.


NEW QUESTION # 22
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

Answer: D

Explanation:
Given the tasks


NEW QUESTION # 23
An organization is deploying generative AI applications on Google Cloud. They want to understand the fundamental security measures integrated into Google ' s global network and hardware. What term specifically describes these inherent protective measures?

Answer: D

Explanation:
Secure-by-design infrastructure means that security protections are incorporated into Google Cloud's hardware, network, and software architecture from their initial design rather than being added only after deployment. Google's infrastructure applies layered controls such as custom hardware security, encryption, identity-based access, network protections, continuous monitoring, and a hardened global architecture. These measures protect workloads and data at multiple levels and provide a secure foundation for generative AI applications. Network protection describes only one part of this broader architecture. Operations management concerns administration and reliability processes, while security oversight refers generally to governance and supervision rather than inherent technical controls. Because the organization is asking for the term covering security measures fundamentally integrated into Google's global network and hardware, secure-by-design infrastructure is the precise and comprehensive answer.


NEW QUESTION # 24
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