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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Google Cloud's generative AI offerings	35%	- Identify the use cases and strengths of Google's foundation models. 1. Veo 2. Imagen 3. Gemini 4. Gemma - Describe Google Cloud's gen AI product and service portfolio. 1. Gemini for Google Cloud 2. Vertex AI 3. Google Workspace 4. Model Garden 5. Vertex AI Studio
Topic 2: Techniques to improve gen AI model output	20%	- Describe how grounding can be used to improve model output. 1. Grounding with Google Search 2. Grounding with enterprise data - Describe prompt engineering techniques and their purpose. 1. Chain of thought 2. One-shot 3. Zero-shot 4. Few-shot - Describe the process of fine-tuning gen AI models. 1. Reinforcement learning from human feedback (RLHF) 2. Supervised tuning
Topic 3: Business strategies for a successful gen AI solution	15%	- Describe change management best practices and their importance. 1. Enabling AI adoption 2. Creating a culture of innovation - Describe Google's approach to responsible AI and its importance. 1. Google's AI principles 2. Responsible AI best practices - Describe best practices for a successful gen AI project. 1. Building a business case 2. Evaluating AI solutions 3. Choosing the right model
Topic 4: Fundamentals of generative AI	30%	- Describe core generative AI (gen AI) concepts and use cases. 1. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost) 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement) 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. 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 - Identify the core layers of the gen AI landscape and the business implications. 1. Applications 2. Platforms 3. Agents 4. Infrastructure 5. Models

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
What is the continuous cycle of observing, interpreting, planning, and acting that makes up an AI agent ' s " thinking process " ?

A. Reasoning loop
B. Model training
C. Platform integration
D. User interface

Answer: A

Explanation:
The reasoning loop is the recurring process through which an AI agent observes its environment or receives information, interprets the current state, plans an appropriate next step, and acts by using a tool or producing a response. The result of that action becomes another observation, allowing the cycle to continue until the agent achieves its objective or reaches a stopping condition. This iterative behavior distinguishes an agent from a model that merely generates a single response. Model training develops or adjusts the model before deployment and is not the agent's runtime thinking cycle. Platform integration connects the agent with systems and services, while the user interface provides a means of interaction. Neither defines the observe- interpret-plan-act process. Consequently, this continuous decision-and-action cycle is correctly identified as the reasoning loop.

NEW QUESTION # 117
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?

A. Generate the video frames with Imagen.
B. Create a video with Google Vids.
C. Prompt the Gemini app to create a video.
D. Train a model with Vertex AI and produce a video.

Answer: B

Explanation:
The scenario requires quick creation of a training video using a custom avatar and voiceover while adhering to zero cost for production.
Google Vids is an AI-powered video creation app (part of Google Workspace/Gemini features) designed to make video creation accessible for teams without the overhead of traditional production. It specifically offers features like AI avatars and voiceovers for content such as trainings, demos, and onboarding videos. This directly addresses the need for a low-cost, fast solution for a new hire training video with custom branding elements (custom avatars and voiceovers are a key feature of the tool).
Option A, Imagen, is a Google foundation model specialized for image generation, not the creation of structured, narrated training videos with avatars. Option B, using the Gemini app, is primarily for text, code, and multimodal chat/generation, and is not the dedicated Google application for video production. Option C, training a model with Vertex AI, is a highly technical, time-consuming, and expensive endeavor that violates the need for a quick and zero-cost solution. Therefore, using the purpose-built, gen AI-enabled Google Vids application is the correct and most efficient choice.

NEW QUESTION # 118
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?

A. Generate the video frames with Imagen.
B. Create a video with Google Vids.
C. Prompt the Gemini app to create a video.
D. Train a model with Vertex AI and produce a video.

Answer: B

NEW QUESTION # 119
What is a primary benefit of using a multi-agent system?

A. To serve as a platform for hosting traditional, non-AI applications.
B. To manage complex tasks that demand coordinated AI functions.
C. To consolidate all unique AI functions into a single, undifferentiated model.
D. To simplify the most basic and repetitive rule-based tasks.

Answer: B

Explanation:
Multi-agent systems are designed to tackle complex problems by breaking them down into sub- tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.

NEW QUESTION # 120
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?

A. Implementing stricter data security measures to protect applicants' financial information from unauthorized access.
B. Ensuring AI decision-making is explainable to understand decision reasons and establish accountability.
C. Increasing the speed at which the AI system processes loan applications to handle the high volume.
D. Regularly updating the AI model with more financial data to improve its accuracy over time.

Answer: B

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.
Options A, C, and D address security, speed, and accuracy, respectively, but Explainability is the direct mechanism for proving fairness and ensuring accountability, making it the most critical priority in this scenario.
(Reference: Google's Responsible AI principles and training materials highlight that in high-stakes domains like finance, explainability is essential for establishing trust, identifying and mitigating bias, and meeting regulatory compliance.)

NEW QUESTION # 121
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