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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Vertex AI Studio
  • 2. Model Garden
  • 3. Google Workspace
  • 4. Gemini for Google Cloud
  • 5. Vertex AI
- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemini
  • 2. Imagen
  • 3. Veo
  • 4. Gemma
Techniques to improve gen AI model output20%- Describe the process of fine-tuning gen AI models.
  • 1. Supervised tuning
  • 2. Reinforcement learning from human feedback (RLHF)
- Describe prompt engineering techniques and their purpose.
  • 1. Few-shot
  • 2. Chain of thought
  • 3. One-shot
  • 4. Zero-shot
- Describe how grounding can be used to improve model output.
  • 1. Grounding with Google Search
  • 2. Grounding with enterprise data
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. Enabling AI adoption
  • 2. Creating a culture of innovation
- Describe best practices for a successful gen AI project.
  • 1. Building a business case
  • 2. Choosing the right model
  • 3. Evaluating AI solutions
Fundamentals of generative AI30%- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between labeled and unlabeled data
  • 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 structured and unstructured data, and identifying real world examples of each type
- 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 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)
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Platforms
  • 2. Infrastructure
  • 3. Applications
  • 4. Agents
  • 5. Models

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
An engineering team at example.com spends about 12 hours each week producing boilerplate and scaffolding for routine service endpoints, and they want an AI capability that can generate this repetitive code from concise requirements or from established patterns so the developers can concentrate on complex tasks. What primary use of generative AI does this scenario represent?

Answer: B

Explanation:
The scenario describes generating boilerplate and scaffolding from concise requirements or established patterns so developers can focus on more complex work.
This use case aligns with generative AI that produces source code from prompts or patterns. It automates repetitive implementation details, accelerates service setup, and helps teams reduce time spent on routine endpoint creation while maintaining consistency.


NEW QUESTION # 14
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: A

Explanation:
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
Options A, B, and C address different aspects of model output: Role prompting sets the model's persona, adjusting temperature controls creativity, and prompt chaining manages conversation history, but none of these techniques restrict the model's source of truth to the official documentation. Therefore, Grounding is the correct and most effective technique for this requirement.


NEW QUESTION # 15
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model ' s summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?

Answer: C

Explanation:
The core problem is the model ' s hallucination-it invented a factual detail-in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D).
Grounding connects the Large Language Model ' s (LLM ' s) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval-Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).
Option B, fine-tuning, is expensive and only updates the model ' s general knowledge and style; it does not prevent the model from guessing or fabricating details when retrieving information. Option C, increasing temperature, would make the output less consistent and more diverse, likely increasing the chance of hallucination, which is the opposite of the desired effect. Option A is unrelated to factual accuracy. Therefore, Grounding is the necessary step to anchor the model ' s responses to the true content of the source articles.
(Reference: Google Cloud documentation on RAG/Grounding emphasizes that its primary purpose is to address the "knowledge cutoff" and hallucination issues of LLMs by retrieving relevant, up-to-date information from external knowledge sources and using this retrieved information to ground the LLM ' s generation, ensuring factual accuracy.)


NEW QUESTION # 16
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?

Answer: A

Explanation:
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.
________________________________________


NEW QUESTION # 17
A marketing agency with a large digital asset library needs a Google Cloud solution to quickly and accurately search its digital files based on visual, spoken, or thematic content. What Google Cloud product should the agency use?

Answer: C

Explanation:
Google Cloud's Media Search (part of Vertex AI Search / Agent Search for media) is specialized for ingesting, indexing, and retrieving multimedia content-such as video, audio, and visual assets-by understanding spoken dialogue, visual actions, and semantic themes across unstructured digital libraries. Search for commerce and Vision API Product search are tailored to e-commerce product catalogs, while Document search focuses on text-heavy formats (PDFs, docs).


NEW QUESTION # 18
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