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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q33-Q38):
NEW QUESTION # 33
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 pre-built gen AI chatbots for inventory questions.
- C. Build a custom API instead of using the gen AI agent.
- D. Use Google Cloud databases and Vertex AI for the agent to get live data.
Answer: D
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 # 34
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?
- A. Customer service agent
- B. Data agent
- C. Security agent
- D. Code agent
Answer: C
Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
________________________________________
NEW QUESTION # 35
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?
- A. Vertex AI Pipelines
- B. BigQuery
- C. Model Registry
- D. Cloud Storage
Answer: C
Explanation:
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.
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NEW QUESTION # 36
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 uses human oversight to ensure accuracy before presenting information to the customer.
- C. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
- D. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
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 # 37
What is an example of unsupervised machine learning?
- A. Analyzing customer purchase patterns to identify natural groupings.
- B. Training a system to recognize product images using labeled categories.
- C. Predicting subscription renewal based on past renewal status data.
- D. Forecasting sales figures using historical sales and marketing spend.
Answer: A
Explanation:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
________________________________________
NEW QUESTION # 38
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