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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q44-Q49):
NEW QUESTION # 44
At mcnz.com your AI team wants one versatile model that they can prompt or fine tune to handle text generation, multilingual translation, and question answering across 18 languages for three product lines. What is the term for a large pretrained model that serves as a general purpose starting point for many downstream applications?
- A. Vertex AI Model Garden
- B. Task-specific model
- C. Foundation model
- D. Reinforcement learning agent
Answer: C
Explanation:
A foundation model is a large pretrained model that serves as a general purpose starting point that you can adapt through prompting or fine tuning for many downstream applications. It is intended to handle varied natural language tasks such as text generation, multilingual translation, and question answering across many languages. This versatility matches the team's requirement for one model that supports multiple product lines and tasks.
NEW QUESTION # 45
An organization with a team of live customer service agents wants to improve agent efficiency and customer satisfaction during support interactions. They are looking for a tool that can provide real-time guidance to agents, suggest helpful information, and streamline the support process without fully automating customer conversations. Which component of Google's Customer Engagement Suite should they use?
- A. Conversational Insights
- B. Google Cloud Contact Center as a Service
- C. Conversational Agents
- D. Agent Assist
Answer: D
Explanation:
As previously mentioned, Agent Assist is specifically designed for real-time support to human agents, providing them with suggestions and relevant information during live customer interactions. Conversational Agents (chatbots) automate interactions, Conversational Insights analyze conversations after they occur, and Contact Center as a Service is the broader infrastructure.
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NEW QUESTION # 46
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?
- A. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
- B. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
- C. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
- D. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
Answer: C
Explanation:
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open-source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
NEW QUESTION # 47
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. Veo
- C. Gemini
- D. Gemma
Answer: D
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 # 48
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. Develop fairness assessments for the gen AI model.
- B. Fine-tune the gen AI model.
- C. Implement explainable gen AI policies.
- D. Collect a larger and more diverse dataset for the gen AI model.
Answer: C
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 # 49
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