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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. Evaluating AI solutions
  • 2. Building a business case
  • 3. Choosing the right model
- 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 prompt engineering techniques and their purpose.
  • 1. Chain of thought
  • 2. Zero-shot
  • 3. Few-shot
  • 4. One-shot
- 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 enterprise data
  • 2. Grounding with Google Search
Google Cloud's generative AI offerings35%- Identify the use cases and strengths of Google's foundation models.
  • 1. Imagen
  • 2. Gemini
  • 3. Gemma
  • 4. Veo
- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Google Workspace
  • 2. Gemini for Google Cloud
  • 3. Vertex AI Studio
  • 4. Vertex AI
  • 5. Model Garden
Fundamentals of generative AI30%- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Models
  • 2. Agents
  • 3. Platforms
  • 4. Applications
  • 5. Infrastructure
- 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. 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)

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Google Cloud Certified - Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q115-Q120):

115. Frage
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

Antwort: C

Begründung:
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.
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116. Frage
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?

Antwort: C

Begründung:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine- tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.


117. Frage
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?

Antwort: C

Begründung:
An AI agent, especially in the context of generative AI, is designed to be more autonomous and capable than a simple model. Its function is to understand a goal, analyze a situation, leverage various tools (including other generative AI models or external APIs), and make decisions or take actions to achieve that goal, often with minimal human intervention.
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118. Frage
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

Antwort: C

Begründung:
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.


119. Frage
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

Antwort: C

Begründung:
The core problem is the lack of reasons for rejection, leading to customer complaints. This falls under the domain of explainable AI (XAI). Implementing explainable gen AI policies or mechanisms would allow the institution to provide transparency into how the AI made its decision, addressing the customer complaints directly. While other options might improve the model, they don't directly solve the transparency issue.
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120. Frage
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