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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Fundamentals of generative AI	30%	- Identify the core layers of the gen AI landscape and the business implications. 1. Models 2. Applications 3. Infrastructure 4. Agents 5. Platforms - 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. 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. 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. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format) 2. Identifying the differences between structured and unstructured data, and identifying real world examples of each type 3. Identifying the differences between labeled and unlabeled data
Topic 2: Techniques to improve gen AI model output	20%	- 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. Supervised tuning 2. Reinforcement learning from human feedback (RLHF) - Describe prompt engineering techniques and their purpose. 1. Few-shot 2. Zero-shot 3. One-shot 4. Chain of thought
Topic 3: Google Cloud's generative AI offerings	35%	- Identify the use cases and strengths of Google's foundation models. 1. Gemini 2. Imagen 3. Veo 4. Gemma - Describe Google Cloud's gen AI product and service portfolio. 1. Vertex AI 2. Vertex AI Studio 3. Google Workspace 4. Model Garden 5. Gemini for Google Cloud
Topic 4: Business strategies for a successful gen AI solution	15%	- 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

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?

A. RAG uses human oversight to ensure accuracy before presenting information to the customer.
B. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
C. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
D. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.

Answer: D

Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
________________________________________

NEW QUESTION # 11
At mcnz.com your AI team wants one versatile model that they can prompt or fine tune to handle text generation, multilingual translation, and question answering across 18 languages for three product lines. What is the term for a large pretrained model that serves as a general purpose starting point for many downstream applications?

A. Vertex AI Model Garden
B. Task-specific model
C. Reinforcement learning agent
D. Foundation model

Answer: D

Explanation:
A foundation model is a large pretrained model that serves as a general purpose starting point that you can adapt through prompting or fine tuning for many downstream applications. It is intended to handle varied natural language tasks such as text generation, multilingual translation, and question answering across many languages. This versatility matches the team's requirement for one model that supports multiple product lines and tasks.

NEW QUESTION # 12
A company is trying to decide which platform to use to optimize its generative AI (gen AI) solutions. Why should the company use Vertex AI Platform?

A. It provides a unified platform of tools for building, deploying, and managing machine learning.
B. It provides gen AI coding assistance with enterprise security and privacy protection.
C. It provides a mechanism for efficient analysis and exploration of large datasets used in machine learning.
D. It provides scalable and cost-effective object storage for data used in machine learning workflows.

Answer: A

Explanation:
Vertex AI is Google Cloud's core, end-to-end Machine Learning Operations (MLOps) platform, designed to cover the entire ML lifecycle.
The key benefit of Vertex AI, particularly for generative AI, is that it provides a unified platform (D) where all stages of AI development-from accessing foundation models in Model Garden, testing in Vertex AI Studio, training and tuning (via tools like Reinforcement Learning from Human Feedback), to deploying, and monitoring models in production-can be managed from a single service. This significantly reduces complexity, improves collaboration between teams (data scientists, engineers, business leaders), and ensures enterprise-grade governance and scalability necessary for production Gen AI solutions.
Option A describes BigQuery.
Option B describes Gemini Code Assist.
Option C describes Cloud Storage.
Vertex AI is the overarching platform that integrates all these tools to deliver a streamlined MLOps workflow.
(Reference: Google Cloud documentation states that Vertex AI is the unified AI development platform that brings together Google Cloud services for building, deploying, and managing machine learning models and generative AI solutions.)

NEW QUESTION # 13
A large company is creating their generative AI (gen AI) solution by using Google Cloud's offerings. They want to ensure that their mid-level managers contribute to a successful gen AI rollout by following Google-recommended practices. What should the mid-level managers do?

A. Drive gen AI adoption by identifying high-impact, feasible solutions that address specific challenges within their workflows.
B. Create a robust data strategy to ensure teams can access high-quality, relevant data that is appropriate for training and fine-tuning gen AI models.
C. Secure funding and resources for AI initiatives by demonstrating the potential return on investment to the chief financial officer (CFO).
D. Perform continuous testing, measurement, and refinement based on user feedback and real- world performance data.

Answer: A

Explanation:
Google's recommended strategy for a successful generative AI rollout involves a combination of top-down strategic alignment and bottom-up adoption. In this structure, the role of the mid-level manager is critical for driving tangible value within their specific domain.

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?

A. Use prompt chaining.
B. Adjust the temperature parameter.
C. Use grounding.
D. Use role prompting.

Answer: C

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 # 15
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