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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

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 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

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 (Q38-Q43):

NEW QUESTION # 38
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?

A. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
B. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
C. RAG uses human oversight to ensure accuracy before presenting information to the customer.
D. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.

Answer: A

Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
________________________________________

NEW QUESTION # 39
What is a primary benefit of using a multi-agent system?

A. To manage complex tasks that demand coordinated AI functions.
B. To simplify the most basic and repetitive rule-based tasks.
C. To serve as a platform for hosting traditional, non-AI applications.
D. To consolidate all unique AI functions into a single, undifferentiated model.

Answer: A

Explanation:
Multi-agent systems are designed to tackle complex problems by breaking them down into sub-tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.
________________________________________

NEW QUESTION # 40
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?

A. Use role prompting.
B. Use prompt chaining.
C. Use few-shot prompting.
D. Adjust the temperature parameter.

Answer: A

Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
________________________________________

NEW QUESTION # 41
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

A. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
B. Google Cloud's comprehensive training materials and tutorials to help developers.
C. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
D. Google Cloud's pre-trained models and low- and no-code AI tools and services.

Answer: D

Explanation:
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
________________________________________

NEW QUESTION # 42
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?

A. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
B. To limit the maximum text length that the model generates by ensuring concise responses.
C. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
D. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.

Answer: D

Explanation:
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.
________________________________________

NEW QUESTION # 43
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