Generative-AI-Leader Free Study Guide! with New Update 47 Exam Questions [Q18-Q34]

Generative-AI-Leader Free Study Guide! with New Update 47 Exam Questions

Get up-to-date Real Exam Questions for Generative-AI-Leader UPDATED [2025]

NEW QUESTION # 18
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?

• A. The Gemini app is the primary Google Cloud tool for directly collecting data.
• B. Google Cloud relies on Vertex AI to connect to external data.
• C. Google Cloud's strengths are in the data analysis tools such as BigQuery.
• D. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.

Answer: D

Explanation:
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
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NEW QUESTION # 19
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?

• A. BigQuery
• B. Vertex AI Natural Language API
• C. Document AI
• D. Grounding with Google Search

Answer: D

Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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NEW QUESTION # 20
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?

• A. Unsupervised learning
• B. Deep learning
• C. Reinforcement learning
• D. Supervised learning

Answer: A

Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify
"anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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NEW QUESTION # 21
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

• A. Vertex AI Pipelines
• B. BigQuery
• C. Cloud Storage
• D. Model Registry

Answer: D

Explanation:
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.
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NEW QUESTION # 22
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertiseand need a versatile solution.
Which Google foundation model should they use?

• A. Gemini
• B. Gemma
• C. Veo
• D. Imagen

Answer: A

Explanation:
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise.
Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
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NEW QUESTION # 23
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

• A. Customer service agent
• B. Security agent
• C. Code agent
• D. Data agent

Answer: B

Explanation:
Given the tasks

NEW QUESTION # 24
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?

• A. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
• B. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
• C. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
• D. To limit the maximum text length that the model generates by ensuring concise responses.

Answer: C

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.
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NEW QUESTION # 25
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?

• A. Unsupervised learning
• B. Deep learning
• C. Supervised learning
• D. Reinforcement learning

Answer: D

Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
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NEW QUESTION # 26
What is a primary benefit of using a multi-agent system?

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

Answer: C

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.
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NEW QUESTION # 27
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?

• A. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
• B. Use Gemini for Google Workspace to facilitate collaborative document review.
• C. Use Vertex AI Agent Builder to create a custom AI agent.
• D. Use Vertex AI Search to index the papers and enable keyword-based searches.

Answer: C

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.
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NEW QUESTION # 28
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?

• A. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
• B. Google Cloud's comprehensive training materials and tutorials to help developers.
• C. Google Cloud's pre-trained models and low- and no-code AI tools and services.
• D. Google Cloud's collaborative AI community and support forums connect developers with AI experts.

Answer: C

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.
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NEW QUESTION # 29
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?

• A. Top-down strategy
• B. Bottom-up strategy
• C. Rapid implementation strategy
• D. Multi-directional strategy

Answer: A

Explanation:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
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NEW QUESTION # 30
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents spend too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support.
What Google Cloud solution should they use?

• A. Vertex AI Conversation
• B. Vertex AI Natural Language API
• C. Pre-built RAG with Vertex AI Search
• D. Vertex AI Model Garden

Answer: C

Explanation:
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use thisindexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
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NEW QUESTION # 31
What is the definition of generative AI?

• A. A type of predictive model that estimates a relationship by fitting a line to the observed data.
• B. A type of machine learning algorithm inspired by the human brain that is made up of interconnected nodes.
• C. A type of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning.4
• D. A type of artificial intelligence that can create new content and ideas, including text, images, music, and code.

Answer: D

Explanation:
The defining characteristic of generative AI is its ability to create new, original content that resembles its training data. This includes various modalities like text, images, music, and code, rather than just classifying, predicting, or analyzing existing data.
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NEW QUESTION # 32
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?

• A. Use role prompting.
• B. Use prompt chaining.
• C. Use few-shot prompting.
• D. Use zero-shot prompting.

Answer: B

Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to "remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
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NEW QUESTION # 33
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?

• A. A customized learning agent
• B. An AI-powered recommendation system for learning resources
• C. A large language model fine-tuned on educational content
• D. A learning management system (LMS)

Answer: A

Explanation:
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
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NEW QUESTION # 34
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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 2	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 3	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
Topic 4	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.

 

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