Google Cloud Certified - Generative AI Leader Exam練習問題の様々な割引

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Google Generative-AI-Leader 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
トピック 2	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
トピック 3	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
トピック 4	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.

参照：https://cloud.google.com/developers/generative-ai-leader

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Google Cloud Certified - Generative AI Leader 認定 Generative-AI-Leader 試験問題：

問題 #1
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

A. To draft emails and provide real-time insights about customer needs.
B. To replace the sales team's CRM system with a more intuitive and user-friendly interface.
C. To automate creative content like blog posts and social media updates to attract new leads.
D. To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.

問題 #2
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?

A. Quantitative data
B. Unstructured data
C. Structured data
D. Labeled data

問題 #3
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?

A. Ensuring that the AI model is trained on a large and diverse dataset.
B. Focusing on collecting only quantitative feedback data in future surveys.
C. Applying data anonymization techniques to remove or obscure sensitive data.
D. Implementing strong access controls to limit which teams can view the raw survey data.

問題 #4
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?

A. Top-down strategy
B. Bottom-up strategy
C. Multi-directional strategy
D. Rapid implementation strategy

問題 #5
A market research firm wants to use a Google Cloud prebuilt generative AI offering to streamline the process of extracting and synthesizing information from lengthy market reports and research papers. Their goal is to improve efficiency and provide faster insights to their clients. What should the organization do?

A. Use NotebookLM to upload the files to extract insights.
B. Build custom conversational agents to interact with documents and extract information.
C. Use the Gemini app to get a general overview of market trends.
D. Use Google Workspace with Gemini to help team members draft summaries and reports.

解説：

問題 #1
正解： A

問題 #2
正解： B

問題 #3
正解： C

問題 #4
正解： A

問題 #5
正解： A