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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
A travel app asks users to take a photo of a famous landmark and then returns a written overview with historical notes and nearby attractions. The system's capability to interpret the picture and produce natural language output reflects what kind of model?

Answer: A

Explanation:
This scenario requires understanding visual content from a photo and then generating a textual explanation. That means the system consumes one modality as an image and produces another modality as text. This cross-modality capability is exactly what a multimodal approach provides, since it jointly handles vision and language to produce coherent natural language output based on visual input.


NEW QUESTION # 27
A company is exploring Gemini Enterprise (Agentspace) to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Gemini Enterprise (Agentspace)?

Answer: C

Explanation:
Gemini Enterprise (Agentspace) is designed as an enterprise-grade AI environment built to solve information fragmentation and employee productivity issues.
The key business advantage of this platform is improved productivity and data interaction using AI assistants and advanced document analysis (C). Agentspace enables organizations to centralize access to internal knowledge bases, document repositories, and communication channels. Employees can use conversational AI assistants to immediately query vast libraries of unstructured corporate data, extract key performance metrics, summarize massive compliance documents, and execute workflow automations without leaving their primary working environment. This drastically reduces time spent manually tracking down information across fragmented tools.
* Option A relates to Identity and Access Management (IAM) or basic data governance controls, which are prerequisite security frameworks rather than the unique business value proposition of Agentspace.
* Option B describes a communication tool like Google Chat or Slack.
* Option D describes specialized middleware or enterprise service buses (ESB), whereas Agentspace focuses on intelligent interaction and synthesis layer rather than base database protocol interoperability.
(Reference: Google Cloud Workspace and Gemini Enterprise strategic whitepapers state that Agentspace serves as a centralized hub that transforms employee workflows by embedding conversational AI assistants into corporate data repositories, unlocking advanced document analysis to maximize knowledge worker velocity and overall productivity.)


NEW QUESTION # 28
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively.
What machine learning should the company use?

Answer: D


NEW QUESTION # 29
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?

Answer: B

Explanation:
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.


NEW QUESTION # 30
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

Answer: A

Explanation:
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.


NEW QUESTION # 31
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