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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
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. Imagen
B. Gemini
C. Gemma
D. Veo

Answer: C

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.
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NEW QUESTION # 14
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 pre-trained models and low- and no-code AI tools and services.
C. Google Cloud's comprehensive training materials and tutorials to help developers.
D. Google Cloud's collaborative AI community and support forums connect developers with AI experts.

Answer: B

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 # 15
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 prompt chaining.
B. Use role prompting.
C. Adjust the temperature parameter.
D. Use few-shot prompting.

Answer: B

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 # 16
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?

A. Focusing on minimizing the processing time for each application to improve efficiency.
B. Ensuring that the AI application can automatically rank all candidates without requiring human review.
C. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
D. Integrating the AI application with various job boards to maximize candidate reach.

Answer: C

Explanation:
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.
________________________________________

NEW QUESTION # 17
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 fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
B. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
C. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
D. RAG uses human oversight to ensure accuracy before presenting information to the customer.

Answer: C

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 # 18
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