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[General] Generative-AI-Leader Certification Dump | Generative-AI-Leader Exam Simulator

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【General】 Generative-AI-Leader Certification Dump | Generative-AI-Leader Exam Simulator

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q24-Q29):NEW QUESTION # 24
What are core hardware components of the infrastructure layer in the generative AI landscape?
  • A. TPUs and GPUs
  • B. Tools and services for building AI models
  • C. User interfaces
  • D. Pre-trained models
Answer: A
Explanation:
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high-volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom-designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.
Options B (User interfaces) and D (Tools and services) refer to the Application and Platform layers, respectively.
Option C (Pre-trained models) refers to the Model layer.
The physical hardware underpinning these abstract layers are the TPUs and GPUs.
(Reference: Google Cloud Generative AI Study Guides state that the Infrastructure Layer provides the core computing resources needed for generative AI, including the physical hardware (like servers, GPUs, and TPUs) and the essential software needed to train, store, and run AI models.)

NEW QUESTION # 25
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?
  • A. Supervised learning
  • B. Deep learning
  • C. Unsupervised learning
  • D. Reinforcement learning
Answer: C
Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify
"anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
________________________________________

NEW QUESTION # 26
A large e-commerce company with a substantial product catalog and many support documents has customers struggling to find information on their website. This leads to high support costs and poor user experience. The company wants a Google Cloud solution to improve website search and reduce support costs while improving customer satisfaction. What Google Cloud product should the company use?
  • A. Google Search
  • B. Google Shopping
  • C. Vertex AI Platform
  • D. Vertex AI Search
Answer: D
Explanation:
Vertex AI Search is ideal for this scenario. It allows companies to build sophisticated search experiences over their own product catalogs and support documents. This improves accuracy and helps customers find what they need, directly addressing high support costs and poor user experience. Vertex AI Platform is broader for general ML development, Google Shopping is for consumers, and Google Search is for the public web.
________________________________________

NEW QUESTION # 27
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. Implement explainable gen AI policies.
  • C. Collect a larger and more diverse dataset for the gen AI model.
  • D. Fine-tune the gen AI model.
Answer: B
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 # 28
What does Vertex AI Search enable companies to do?
  • A. To compare products from numerous online retailers, allowing users to find the best deals and product options across the internet.
  • B. To surface the most popular and frequently accessed content based on global user search patterns and trends.
  • C. To ground LLM responses with first-party data, third-party data, and Google's knowledge graph.
  • D. To index and retrieve information from the entire public web, providing a comprehensive view of publicly available data.
Answer: C
Explanation:
Vertex AI Search is designed to enable powerful search experiences over an organization's own data (first- party), external data (third-party), and can leverage Google's knowledge graph to provide more relevant and accurate responses, especially when grounding Large Language Models (LLMs). It does not index the entire public web like Google Search.
________________________________________

NEW QUESTION # 29
......
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