Generative-AI-Leader Unterlage - Generative-AI-Leader Examsfragen

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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q116-Q121):

116. Frage
A marketing team wants to use a generative AI model to create product descriptions for their new line of eco-friendly water bottles. They provide a brief prompt stating, "Write a product description for our new water bottle." The model generates a generic, lackluster description that is factually accurate but lacks engaging language and doesn't highlight the environmental benefits that are key to their brand. What should the marketing team do to overcome this limitation of the generated product description?

Antwort: C

Begründung:
The core problem described is a lackluster and generic output that fails to capture the desired tone and key information (environmental benefits). This is a classic limitation of zero-shot prompting (a brief, un-detailed prompt), where the generative AI model relies solely on its general training data and lacks the necessary context to produce a highly relevant and engaging response. The solution is to improve the quality of the prompt itself, a process known as Prompt Engineering.


117. Frage
A company is evaluating different generative AI (gen AI) platforms and wants to understand the role of the infrastructure layer in supporting the development and deployment of gen AI models. What is the function of the infrastructure layer in the gen AI landscape?

Antwort: A

Begründung:
The infrastructure layer supplies the foundational computing, storage, networking, and acceleration resources required to train and run generative AI models. This includes CPUs, GPUs, TPUs, high-performance networks, scalable storage, and systems optimized for demanding AI workloads. Training foundation models and serving model responses require substantial processing capacity, while training datasets and model artifacts require reliable storage. Access to pre-trained models belongs primarily to the model layer. A user- friendly model interface is part of the application or experience layer. Development, deployment, tuning, and management tools belong to the platform layer. These layers work together, but their functions are distinct.
Because the question asks specifically about the infrastructure layer, the correct function is supplying the computational resources and data storage needed to train and operate AI models.


118. Frage
A company uses a generative AI model to create campaign messaging. However, the newly trained version of the model is more creative but less aligned with the brand voice than the previous version. The marketing team must decide which model to use and potentially revert to the prior model if the new one consistently underperforms in brand alignment. What Google-recommended model management practice should they use?

Antwort: A

Begründung:
Model versioning (supported via tools like Vertex AI Model Registry) allows machine learning teams to catalog, track, compare, and roll back deployed model iterations. When a newer version of a model exhibits behavioral regressions or deviates from specific requirements (such as brand voice), model versioning provides the operational mechanism to maintain lineage and quickly revert to the proven earlier version in production.


119. Frage
A development team is building an internal knowledge base chatbot to answer employee questions about company policies and procedures. This information is stored across various documents in Google Cloud Storage and is updated regularly by different departments. What is the primary benefit of using Google Cloud's RAG APIs in this scenario?

Antwort: B

Begründung:
The primary benefit of RAG (Retrieval-Augmented Generation) in this context is its ability to ensure the chatbot provides accurate and up-to-date information. By retrieving relevant and recent policy documents from Cloud Storage in real-time and then grounding the LLM's response with this information, the chatbot avoids hallucinating or providing outdated answers, which is crucial for an internal knowledge base.
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120. Frage
A global news agency is developing a generative AI tool to quickly summarize breaking news articles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?

Antwort: A

Begründung:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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121. Frage
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