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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q17-Q22):
NEW QUESTION # 17
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?
- A. Grounding with Google Search
- B. BigQuery
- C. Vertex AI Natural Language API
- D. Document AI
Answer: A
Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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NEW QUESTION # 18
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. Collect a larger and more diverse dataset for the gen AI model.
- D. Implement explainable gen AI policies.
Answer: D
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 # 19
What is a primary benefit of using a multi-agent system?
- A. To simplify the most basic and repetitive rule-based tasks.
- B. To manage complex tasks that demand coordinated AI functions.
- 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: B
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 # 20
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?
- A. Improved IT infrastructure management across offices.
- B. Enhanced data encryption and compliance for internal communications.
- C. Automation of employee performance reviews using AI.
- D. Seamless knowledge sharing and collaboration across internal systems.
Answer: D
Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
________________________________________
NEW QUESTION # 21
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory data. They want the most cost-effective solution. What should the organization do?
- A. Use Vertex AI Studio to fine-tune a model with sample inventory data.
- B. Use Google Cloud databases and Vertex AI for the agent to get live data.
- C. Use pre-built gen AI chatbots for inventory questions.
- D. Build a custom API instead of using the gen AI agent.
Answer: B
Explanation:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.
NEW QUESTION # 22
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