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| Section | Objectives |
|---|---|
| Topic 1: Business Applications and Adoption Strategy | - AI-driven transformation and workflow integration - Identifying business use cases for generative AI - Measuring ROI and value of generative AI initiatives |
| Topic 2: Fundamentals of Generative AI | - Core concepts of generative AI and large language models - Difference between traditional AI, machine learning, and generative AI - Key use cases and limitations of generative AI |
| Topic 3: Google Cloud Generative AI Products and Tools | - AI APIs and model deployment options on Google Cloud - Prompt design and prompt engineering tools - Vertex AI and Gemini models overview |
| Topic 4: Responsible AI and Governance | - Data privacy and security in generative AI systems - AI safety, bias, and fairness considerations - Responsible AI principles and compliance |
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NEW QUESTION # 78
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human-like conversations and provide accurate information. What should they do to enhance the chatbot's ability to understand and respond effectively to user prompts?
Answer: B
Explanation:
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input-output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human-like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
NEW QUESTION # 79
A nonprofit news analytics lab is building a generative AI platform and wants the freedom to combine open-source models and tooling with managed Google Cloud services. They want to minimize vendor lock-in and benefit from innovations created by the wider AI community. Which characteristic of Google Cloud's generative AI strategy would most appeal to this lab?
Answer: A
Explanation:
This approach directly aligns with the lab's goal to combine open source models and community tooling with managed Google Cloud services while keeping portability. It reduces vendor lock in by allowing teams to run and swap models across environments, benefit from community innovations, and integrate with services like Vertex AI and GKE without being tied to a single proprietary stack. Interoperability across frameworks and infrastructure is the central advantage that addresses the scenario requirements.
NEW QUESTION # 80
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: A
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.
NEW QUESTION # 81
A company needs a versatile AI model for tasks like drafting emails, summarizing documents, generating images, and assisting with code to improve efficiency across departments. What is the main advantage of using Gemini for this use case?
Answer: A
Explanation:
Gemini's principal advantage in this scenario is its multimodal capability. Gemini models can understand and generate content across multiple formats, including text, images, and code. This versatility allows one model family to support email drafting, document summarization, image generation workflows, and software- development assistance across different departments. Gemini is not completely open-source, eliminating option A. Although Gemini can support the creation of AI agents, agent customization is not the defining benefit connecting all the tasks described. Specialized business-intelligence analysis is also narrower than the organization's cross-functional requirements. The question emphasizes several different content types and activities rather than one specialized workflow. Therefore, Gemini's ability to work with text, visual information, and code provides the broadest and most directly relevant advantage for improving organizational efficiency.
NEW QUESTION # 82
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: D
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 # 83
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