Generative-AI-Leader Echte Fragen, Generative-AI-Leader Musterprüfungsfragen

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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Business strategies for a successful gen AI solution15%- Describe change management best practices and their importance.
  • 1. Enabling AI adoption
  • 2. Creating a culture of innovation
- Describe best practices for a successful gen AI project.
  • 1. Building a business case
  • 2. Choosing the right model
  • 3. Evaluating AI solutions
- Describe Google's approach to responsible AI and its importance.
  • 1. Responsible AI best practices
  • 2. Google's AI principles
Topic 2: Fundamentals of generative AI30%- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 2. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
  • 3. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
  • 4. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between labeled and unlabeled data
  • 2. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
  • 3. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Models
  • 2. Agents
  • 3. Infrastructure
  • 4. Platforms
  • 5. Applications
Topic 3: Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Vertex AI
  • 2. Google Workspace
  • 3. Vertex AI Studio
  • 4. Model Garden
  • 5. Gemini for Google Cloud
- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemini
  • 2. Veo
  • 3. Imagen
  • 4. Gemma
Topic 4: Techniques to improve gen AI model output20%- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
- Describe how grounding can be used to improve model output.
  • 1. Grounding with enterprise data
  • 2. Grounding with Google Search
- Describe prompt engineering techniques and their purpose.
  • 1. Zero-shot
  • 2. Few-shot
  • 3. Chain of thought
  • 4. One-shot

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Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q61-Q66):

61. Frage
What is an example of unsupervised machine learning?

Antwort: B

Begründung:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
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62. Frage
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents spend too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support.
What Google Cloud solution should they use?

Antwort: B

Begründung:
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use thisindexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
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63. Frage
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

Antwort: B

Begründung:
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.


64. Frage
According to Google-recommended practices, when should generative AI be used to augment your strategic thought processes?

Antwort: A

Begründung:
Generative AI is particularly useful as a thought partner for work involving exploration, synthesis, creativity, and strategic decision-making. It can rapidly generate alternatives, summarize complex information, challenge assumptions, identify patterns, and help users examine a problem from multiple perspectives. These capabilities can augment human analysis and content creation, while final judgment remains with the responsible person. Highly sensitive medical or legal decisions require qualified professional oversight and should not be delegated to a model. Financial reporting and accounting demand deterministic accuracy and verification because generative models can hallucinate or calculate incorrectly. Repetitive, rule-based work may be automated, but that is operational automation rather than augmentation of strategic thinking. Option D therefore best reflects the recommended use of generative AI to expand and strengthen human thought processes.


65. Frage
An organization with a team of live customer service agents wants to improve agent efficiency and customer satisfaction during support interactions. They are looking for a tool that can provide real-time guidance to agents, suggest helpful information, and streamline the support process without fully automating customer conversations. Which component of Google's Customer Engagement Suite should they use?

Antwort: A

Begründung:
As previously mentioned, Agent Assist is specifically designed for real-time support to human agents, providing them with suggestions and relevant information during live customer interactions. Conversational Agents (chatbots) automate interactions, Conversational Insights analyze conversations after they occur, and Contact Center as a Service is the broader infrastructure.


66. Frage
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