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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader Certification Exam |
| Exam Number: | Generative-AI-Leader |
| Exam Format: | Multiple choice |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 99 (plus tax where applicable) |
| Passing Score: | Not publicly disclosed |
| Available Languages: | Portuguese, Spanish, Japanese, English |
| Real Exam Qty: | 50-60 |
| Recommended Training: | Generative AI Leader Study Guide Generative AI Leader Training Course |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No prerequisites required; open to all roles and backgrounds |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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NEW QUESTION # 15
A global news agency is developing a generative AI tool to quickly summarize breaking news articles 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?
Answer: B
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 # 16
A company collects customer feedback through open-ended survey questions where customers can write detailed responses in their own words, such as "The product was easy to use, and the customer support was excellent, but the delivery took longer than expected." What type of data is this?
Answer: B
Explanation:
Data is typically classified into two main types: structured and unstructured.
Structured data is highly organized, formatted for a predefined data model, and easily searchable in tabular form (e.g., columns and rows in a database, like customer names, order IDs, or star ratings). Unstructured data lacks a pre-defined format or organization. The customer feedback described is a detailed, free-text response written in the customer's own words. This qualitative data, whether it is an email, an essay, or a long-form survey response, does not fit into fixed fields and requires advanced Natural Language Processing (NLP) or Generative AI techniques to extract meaning. Since the text is non-tabular and has no inherent structure enforced by the collection method, it is correctly classified as Unstructured Data.
NEW QUESTION # 17
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?
Answer: D
Explanation:
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress. This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent.
This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
NEW QUESTION # 18
A home loan company is deploying a generative AI system to automate initial loan application reviews. Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?
Answer: C
Explanation:
The problem centers on unexpected rejections and potential bias in a high-stakes, regulated domain (lending). In such a context, the central tenet of Responsible AI is transparency and fairness.
While all options are valid goals, the priority when facing bias concerns and customer complaints due to rejection is to provide accountability and verify the fairness of the automated decision. This is achieved through Explainable AI (XAI).
Ensuring AI decision-making is explainable (B) means building mechanisms that allow developers, regulators, and affected customers to understand why a specific decision (rejection) was made. Explainability is crucial for:
Auditing for bias: If the reasons for rejection can be traced (e.g., system rejects based on loan-to-value ratio, not race), bias can be identified and corrected.
Compliance: Financial services are heavily regulated, and the ability to explain a lending decision is often a legal or regulatory requirement.
Customer Trust: Providing a clear reason for rejection (even if the news is bad) reduces complaints and fosters confidence, directly addressing the core issue of unexpected rejections.
Options A, C, and D address security, speed, and accuracy, respectively, but Explainability is the direct mechanism for proving fairness and ensuring accountability, making it the most critical priority in this scenario.
(Reference: Google's Responsible AI principles and training materials highlight that in high-stakes domains like finance, explainability is essential for establishing trust, identifying and mitigating bias, and meeting regulatory compliance.)
NEW QUESTION # 19
What is a key advantage of using Google's custom-designed TPUs?
Answer: C
Explanation:
TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs).
TPUs excel at parallel processing (C) for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini).
TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources.
While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models.
NEW QUESTION # 20
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