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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of generative AI30%- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Infrastructure
  • 2. Applications
  • 3. Models
  • 4. Agents
  • 5. Platforms
- 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. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 3. 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)
  • 4. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
- 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. Identifying the differences between labeled and unlabeled data
  • 3. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. One-shot
  • 2. Chain of thought
  • 3. Zero-shot
  • 4. Few-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
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 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. Enabling AI adoption
  • 2. Creating a culture of innovation
Google Cloud's generative AI offerings35%- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemma
  • 2. Veo
  • 3. Gemini
  • 4. Imagen
- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Vertex AI
  • 2. Google Workspace
  • 3. Model Garden
  • 4. Gemini for Google Cloud
  • 5. Vertex AI Studio

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
What is a key advantage of using Google's custom-designed TPUs?

Answer: B

Explanation:
TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs).
TPUs excel at parallel processing (C) for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini).
TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources.
While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models.


NEW QUESTION # 101
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?

Answer: D

Explanation:
Grounding is the technique of "grounding" the LLM's responses in specific, authoritative data sources (like the company's official documentation). This prevents the model from "hallucinating" or providing information outside of the approved knowledge base, ensuring accuracy and relevance to the company's specific products and services.


NEW QUESTION # 102
What does Vertex AI Search enable companies to do?

Answer: B

Explanation:
Vertex AI Search is designed to enable powerful search experiences over an organization's own data (first-party), external data (third-party), and can leverage Google's knowledge graph to provide more relevant and accurate responses, especially when grounding Large Language Models (LLMs). It does not index the entire public web like Google Search.


NEW QUESTION # 103
A home loan company is deploying a generative AI system to automate initial loan application reviews.
Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?

Answer: A


NEW QUESTION # 104
A home loan company is deploying a generative AI system to automate initial loan application reviews. Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?

Answer: A

Explanation:
The problem centers on unexpected rejections and potential bias in a high-stakes, regulated domain (lending). In such a context, the central tenet of Responsible AI is transparency and fairness. While all options are valid goals, the priority when facing bias concerns and customer complaints due to rejection is to provide accountability and verify the fairness of the automated decision. This is achieved through Explainable AI (XAI).
Ensuring AI decision-making is explainable (B) means building mechanisms that allow developers, regulators, and affected customers to understand why a specific decision (rejection) was made.
Explainability is crucial for:
Auditing for bias: If the reasons for rejection can be traced (e.g., system rejects based on loan-to- value ratio, not race), bias can be identified and corrected. Compliance: Financial services are heavily regulated, and the ability to explain a lending decision is often a legal or regulatory requirement.
Customer Trust: Providing a clear reason for rejection (even if the news is bad) reduces complaints and fosters confidence, directly addressing the core issue of unexpected rejections.


NEW QUESTION # 105
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