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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Google Cloud's generative AI offerings35%- Identify the use cases and strengths of Google's foundation models.
  • 1. Veo
  • 2. Gemini
  • 3. Gemma
  • 4. Imagen
- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Google Workspace
  • 2. Gemini for Google Cloud
  • 3. Vertex AI Studio
  • 4. Model Garden
  • 5. Vertex AI
Topic 2: 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 Google's approach to responsible AI and its importance.
  • 1. Google's AI principles
  • 2. Responsible AI best practices
- Describe change management best practices and their importance.
  • 1. Creating a culture of innovation
  • 2. Enabling AI adoption
Topic 3: 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. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 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. 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)
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Infrastructure
  • 2. Applications
  • 3. Agents
  • 4. Platforms
  • 5. Models
- 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)
Topic 4: 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 how grounding can be used to improve model output.
  • 1. Grounding with enterprise data
  • 2. Grounding with Google Search
- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?

Answer: A

Explanation:
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.


NEW QUESTION # 67
What is a definition of an AI agent?

Answer: A

Explanation:
In the generative AI landscape, an AI Agent is distinct from a basic chatbot or standard foundation model because of its ability to act autonomously to execute multi-step objectives.
The correct definition is an application that learns how to achieve a goal based on inputs and tools available to it (C). An agent is built around a core Large Language Model (LLM) which serves as its " brain.
" Given an objective or goal from a user, the agent uses a reasoning loop (such as ReAct) to evaluate inputs, break the goal into sub-tasks, and call external tools (like APIs, web search, databases, or calculators) to interact with the external world and accomplish the task.
* Option A is incorrect because agents are dynamic and action-oriented, not static.
* Option B describes a " Human-in-the-loop " supervisor, not the AI agent itself.
* Option D describes a chat interface or frontend wrapper, which is merely a way to communicate with an agent, not the definition of the agent ' s functional architecture.
(Reference: Google Cloud ' s official architecture guides for Generative AI define an Agent as an autonomous software entity driven by an LLM that accepts natural language goals, breaks them down into executable workflows, and leverages tools to alter states or retrieve information to satisfy that goal.)


NEW QUESTION # 68
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

Answer: B

Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
________________________________________


NEW QUESTION # 69
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?

Answer: B

Explanation:
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.


NEW QUESTION # 70
A global travel booking platform named VistaVoyage is developing a generative AI system to identify payment fraud across about 45 million reservations each day. The team is concerned that adversaries may make small tweaks to inputs so the model incorrectly treats fraudulent behavior as legitimate. At what point in the machine learning lifecycle should robust protections against these adversarial tactics be established to preserve security?

Answer: A

Explanation:
Adversarial robustness needs to be designed into the model from the start and then sustained in production. During training you can harden models with adversarial training, robust data augmentation, regularization, and careful evaluation against adversarial and out of distribution test sets. In production you should continuously monitor for drift, anomalies, and suspicious input patterns and you should feed incidents back into retraining so the system improves over time.
This lifecycle approach ensures protections evolve with attacker tactics and with data and model changes.


NEW QUESTION # 71
......

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