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Google Generative-AI-Leader Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 3	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.
Thema 4	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.

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Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q23-Q28):

23. Frage
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. Deep learning
B. Unsupervised learning
C. Supervised learning
D. Reinforcement learning

Antwort: B

Begründung:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify "anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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24. Frage
An order fulfillment team has an agent that automatically processes orders, updates inventory, sends shipping notifications, and handles returns. What type of agent is this?

A. An employee productivity agent
B. A workflow agent
C. A customer service agent
D. A conversational agent

Antwort: B

Begründung:
Generative AI agents are typically categorized based on the goal they are designed to achieve.
The agent described is performing a sequence of distinct, interconnected, operational tasks (processes orders, updates inventory, sends notifications, handles returns). These steps are typical components of a business workflow or process automation. A Workflow Agent is an AI agent whose purpose is to automate and manage an entire business process or a complex multi- step sequence of operations that traditionally required manual handoffs between different systems or teams. It uses its large language model brain, coupled with tools (such as APIs to a CRM, Inventory database, or shipping system), to observe the state of a process (e.g., a new order), reason about the next step, and execute the necessary actions to move the process forward toward completion.

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

Antwort: B

Begründung:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
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26. Frage
A company has launched a new generative AI-powered chatbot on its customer service portal to handle frequently asked questions. They want to monitor the new chatbot's performance over time to ensure it continues to perform well. What Google-recommended practice should they use?

A. Defining and tracking key performance indicators (KPIs).
B. A/B testing of different chatbot conversation flows.
C. Prompting the chatbot to rate its performance.
D. Using a human-in-the-loop (HITL) review process for chatbot interactions.

Antwort: A

Begründung:
To evaluate and track the sustained performance and business impact of an AI system over time, organizations must establish, measure, and monitor quantitative Key Performance Indicators (KPIs). For customer support agents, standard KPIs include resolution rate, containment rate, deflection rate, latency, user satisfaction (CSAT), and error/escalation rates.

27. Frage
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory dat

A. Use pre-built gen AI chatbots for inventory questions.
B. Build a custom API instead of using the gen AI agent.
C. They want the most cost-effective solution. What should the organization do?
D. Use Google Cloud databases and Vertex AI for the agent to get live data.
E. Use Vertex AI Studio to fine-tune a model with sample inventory data.

Antwort: E

Begründung:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.

28. Frage
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