さらに、Japancert Generative-AI-Leaderダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1QBzFI6OSNw8dat7o9Hw1nFidC2nNRatz
幸せの生活は自分で作られて得ることです。だから、大人気なIT仕事に従事したいあなたは今から準備して努力するのではないでしょうか？さあ、ここで我々社のGoogleのGenerative-AI-Leader試験模擬問題を推薦させてくださいませんか。我が社のGenerative-AI-Leader問題集は必ずあなたの成功へ道の助力になれます。
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>> Generative-AI-Leader資格取得 <<
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質問 # 25
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
正解：A
解説：
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google ' s Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model ' s parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application ' s outputs.
This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn ' t solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation.
(Reference: Google Cloud ' s Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)
質問 # 26
A data analyst at MetroVoyage tests a foundation model by writing the prompt "Translate the phrase 'good evening' into Italian." The instruction is given directly and no example translations are included. Which prompting technique is being applied?
正解：B
解説：
Since the model is asked to translate using only an instruction and no example translations are provided.
In Zero-shot prompting the model relies on its pretraining to perform the task without any demonstrations. The prompt directly states the task which is to translate the phrase 'good evening' into Italian and it contains no sample input output pairs which matches this technique.
質問 # 27
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?
正解：D
解説：
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.
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質問 # 28
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?
正解：B
解説：
Gemini models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
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質問 # 29
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?
正解：A
解説：
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
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質問 # 30
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