Google Generative-AI-Leader 시험요강:

주제	소개
주제 1	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.
주제 2	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.
주제 3	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.
주제 4	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.

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

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최신 Google Cloud Certified Generative-AI-Leader 무료샘플문제

문제 #1
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?

A. Data dependency
B. Knowledge cutoff
C. Hallucination
D. Bias

문제 #2
A large company is creating their generative AI (gen AI) solution by using Google Cloud's offerings. They want to ensure that their mid-level managers contribute to a successful gen AI rollout by following Google-recommended practices. What should the mid-level managers do?

A. Create a robust data strategy to ensure teams can access high-quality, relevant data that is appropriate for training and fine-tuning gen AI models.
B. Perform continuous testing, measurement, and refinement based on user feedback and real-world performance data.
C. Secure funding and resources for AI initiatives by demonstrating the potential return on investment to the chief financial officer (CFO).
D. Drive gen AI adoption by identifying high-impact, feasible solutions that address specific challenges within their workflows.

문제 #3
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. Reinforcement learning
B. Unsupervised learning
C. Deep learning
D. Supervised learning

문제 #4
A team is discussing the different layers of the generative AI (gen AI) landscape and where specific tools and technologies fit within those layers. They want to clarify the role of Gemini Enterprise Agent Platform and data management tools in the overall landscape. In which layer of the generative AI landscape are Gemini Enterprise Agent Platform and data management tools?

A. Agents
B. Models
C. Platform
D. Infrastructure

문제 #5
A software engineering team is experimenting with generative AI within their coding process. What is a benefit of using gen AI to create unit tests for code?

A. Eliminating the need for human intervention.
B. Reducing manual validation effort.
C. Guaranteeing errorless code.
D. Reducing code complexity.

질문과 대답:

문제 #1
정답: C

문제 #2
정답: D

문제 #3
정답: B

문제 #4
정답: C

문제 #5
정답: B

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