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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. Zero-shot
  • 2. Chain of thought
  • 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 Google Search
  • 2. Grounding with enterprise data
Fundamentals of generative AI30%- 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. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 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. 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
- 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
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Applications
  • 2. Agents
  • 3. Platforms
  • 4. Infrastructure
  • 5. Models
Business strategies for a successful gen AI solution15%- 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. Choosing the right model
  • 2. Building a business case
  • 3. Evaluating AI solutions
- Describe Google's approach to responsible AI and its importance.
  • 1. Responsible AI best practices
  • 2. Google's AI principles
Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Model Garden
  • 2. Google Workspace
  • 3. Gemini for Google Cloud
  • 4. Vertex AI Studio
  • 5. Vertex AI
- 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 (Q115-Q120):

NEW QUESTION # 115
What is an example of unsupervised machine learning?

Answer: A

Explanation:
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).


NEW QUESTION # 116
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?

Answer: D

Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to
"remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.


NEW QUESTION # 117
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

Answer: D


NEW QUESTION # 118
A company collects customer feedback through open-ended survey questions where customers can write detailed responses in their own words, such as " The product was easy to use, and the customer support was excellent, but the delivery took longer than expected. " What type of data is this?

Answer: A

Explanation:
Data is typically classified into two main types: structured and unstructured.
Structured data is highly organized, formatted for a predefined data model, and easily searchable in tabular form (e.g., columns and rows in a database, like customer names, order IDs, or star ratings).
Unstructured data lacks a pre-defined format or organization.
The customer feedback described is a detailed, free-text response written in the customer ' s own words. This qualitative data, whether it is an email, an essay, or a long-form survey response, does not fit into fixed fields and requires advanced Natural Language Processing (NLP) or Generative AI techniques to extract meaning.
Since the text is non-tabular and has no inherent structure enforced by the collection method, it is correctly classified as Unstructured Data.
Quantitative data (D) refers to numerical values that can be counted or measured. Labeled data (C) is data that has been tagged with a meaningful output category, which this raw feedback has not yet received.
(Reference: Google ' s Generative AI Study Guides define Unstructured Data as data that does not have a predefined structure or data model, such as text documents, images, audio, and video. Free-text responses in a survey are a primary example of unstructured data.)


NEW QUESTION # 119
An organization is building a generative AI agent for employee travel bookings. The agent needs to connect to external flight and hotel systems for availability, pricing, and reservations. They implement the most simple and effective way for the agent to interact with the external travel provider system, following Google Cloud- recommended practices. What method should they use?

Answer: C

Explanation:
Extensions provide a standardized mechanism through which an AI agent can invoke external APIs and complete real-world actions. A travel extension can connect the agent to flight and hotel services, pass structured parameters, retrieve current availability and prices, and submit reservations. This is more maintainable and consistent than embedding separate custom functions directly inside the agent. Pre-loaded data stores cannot satisfy the requirement because availability and pricing change continuously, while reservations require transactional access to the provider's live system. "Plugins" is not the recommended Google Cloud mechanism identified for this agent-integration scenario. Extensions enable agents to move beyond answering questions by securely communicating with external services through defined interfaces.
Consequently, they are the simplest and most effective Google-recommended method for integrating the travel agent with provider APIs.


NEW QUESTION # 120
......

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