[Sep 10, 2026] New Google Generative-AI-Leader Dumps with Test Engine and PDF (New Questions)
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Google Generative-AI-Leader Exam Syllabus Topics:
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NEW QUESTION # 61
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 Search to index the papers and enable keyword-based searches.
- B. Use Vertex AI Agent Builder to create a custom AI agent.
- C. Use Gemini for Google Workspace to facilitate collaborative document review.
- D. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
Answer: B
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 # 62
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?
- A. Increase the temperature setting of the model to encourage more diverse outputs.
- B. Fine-tune the model on a larger dataset of news articles.
- C. Use grounding to base the model output on the source articles.
- D. Implement stricter safety settings to filter out potentially controversial topics.
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).
NEW QUESTION # 63
According to Google-recommended practices, when should generative AI be used to automate tasks?
- A. When tasks are repetitive and rule-based.
- B. When tasks are complex and require strategic decision-making.
- C. When tasks involve sensitive information or require human oversight
- D. When tasks are highly creative and require original thought.
Answer: A
Explanation:
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning).
Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
Options A and D represent high-value, strategic work-highly creative or complex strategic decision-making-where human judgment and oversight remain paramount. While Gen AI can assist with these (e.g., brainstorming creative ideas or providing data-backed insights), it is generally not recommended for full automation. Option B explicitly requires human oversight due to its sensitive nature. Therefore, the best fit for full or augmented automation for efficiency is the handling of routine, repeatable, and non-complex tasks.
(Reference: Google Cloud documentation on Gen AI adoption and efficiency states that Gen AI transforms work by automating repetitive and time-consuming tasks to free up time for strategic thinking and creativity.)
NEW QUESTION # 64
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. Data agent
- B. Code agent
- C. Customer service agent
- D. Security agent
Answer: D
Explanation:
Given the tasks
NEW QUESTION # 65
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.
NEW QUESTION # 66
A manager wants to ensure that only quality data is used in their AI model. Which scenario is most likely to lead to an unfair and biased outcome?
- A. A technology firm trains a facial recognition system primarily using images of individuals from one demographic group, with limited representation from other groups.
- B. A marketing team creates a personalized recommendation engine based on website clickstream data that includes bot traffic.
- C. A company ' s customer service natural language processing model is trained on a dataset containing incorrectly encoded text from older website versions.
- D. A financial institution builds a fraud detection model using transaction data that is incomplete, lacking details about the merchants involved in a significant percentage of the transactions.
Answer: A
Explanation:
Training a facial-recognition system predominantly on one demographic creates representation bias. The model receives insufficient examples from other groups and will probably perform less accurately for those populations, producing systematically unequal outcomes. This is directly associated with fairness because model performance varies according to demographic characteristics. The other scenarios describe serious data- quality problems, but their primary effects differ. Missing merchant details can reduce fraud-detection accuracy, incorrectly encoded text introduces corruption, and bot traffic distorts customer-behavior signals.
Those defects may degrade overall performance without necessarily disadvantaging a protected or underrepresented group. Responsible AI development requires representative datasets, subgroup-level evaluation, documented data provenance, and ongoing monitoring for unequal error rates. Therefore, the facial-recognition dataset presents the clearest and most direct risk of an unfair and biased outcome.
NEW QUESTION # 67
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 Model Garden
- B. Pre-built RAG with Vertex AI Search
- C. Vertex AI Natural Language API
- D. Vertex AI Conversation
Answer: B
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 this indexed 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 # 68
According to Google-recommended practices, when should generative AI be used to automate tasks?
- A. When tasks are repetitive and rule-based.
- B. When tasks are complex and require strategic decision-making.
- C. When tasks involve sensitive information or require human oversight
- D. When tasks are highly creative and require original thought.
Answer: A
Explanation:
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning). Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
NEW QUESTION # 69
What is a characteristic of Google Cloud as a generative AI company?
- A. Google Cloud provides fully autonomous AI agents that require zero configuration or management overhead.
- B. Google Cloud has an AI-first focus that enables innovation, with continuous updates and broad integration across its platform.
- C. Google Cloud relies on proprietary, closed-source AI technologies for maximum security benefits.
- D. Google Cloud ensures that all generative AI models and data are completely secured and isolated from external networks.
Answer: B
NEW QUESTION # 70
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 Search to index the papers and enable keyword-based searches.
- B. Use Vertex AI Agent Builder to create a custom AI agent.
- C. Use Gemini for Google Workspace to facilitate collaborative document review.
- D. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
Answer: B
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.
NEW QUESTION # 71
An organization is collecting data to train a generative AI model for customer service. They want to ensure security throughout the ML lifecycle. What is a critical consideration at this stage?
- A. Monitoring the AI model's performance for unexpected outputs and potential errors.
- B. Implementing access controls and protecting sensitive information within the training data.
- C. Applying the latest software patches to the AI model on a regular basis.
- D. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
Answer: B
Explanation:
The stage mentioned is Data Collection/Training Data Preparation. In the machine learning lifecycle, this initial stage is where raw data is ingested and processed. If the model is being trained for customer service, the data (e.g., customer transcripts) is highly likely to contain sensitive information (like Personally Identifiable Information or PII).
Therefore, the most critical security and privacy consideration at this stage is protecting the integrity and confidentiality of the data itself.
Implementing strong access controls and protecting sensitive information (A) is the essential first step in a secure AI pipeline, aligning with Google's Secure AI Framework (SAIF). If data access is not controlled and sensitive data is not de-identified or redacted before it is used for training, the resulting model could leak that sensitive information to users.
NEW QUESTION # 72
A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions:
"To address this customer's problem, we should first identify the core issue they are experiencing. Then, we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer's message: 'My order hasn't arrived, and the tracking number shows no updates for a week,' what should be the next step in resolving this?" What type of prompting is this?
- A. Zero-shot
- B. Few-shot
- C. Chain-of-thought
- D. Role-based
Answer: C
Explanation:
The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: "first identify," "then check," "if a solution exists, explain," "if not, escalate." This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis.
Zero-shot (A) would be the raw question without any structure.
Few-shot (B) would involve providing examples of successfully solved problems.
Role-based (C) would involve assigning a persona (e.g., "Act as a customer service expert") but would not explicitly mandate the sequential process.
The inclusion of the explicit steps ("first identify," "then check," etc.) is the defining characteristic of Chain-of-Thought prompting.
(Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)
NEW QUESTION # 73
A company needs a versatile AI model for tasks like drafting emails, summarizing documents, generating images, and assisting with code to improve efficiency across departments. What is the main advantage of using Gemini for this use case?
- A. Specialized data analysis for business intelligence, even for non-technical users.
- B. The ability to handle text, images, and code to support a wide range of tasks.
- C. Being completely open-source for modification without licensing costs.
- D. The ability to easily create highly customized AI agents to automate complex tasks with minimal input.
Answer: B
Explanation:
Gemini's principal advantage in this scenario is its multimodal capability. Gemini models can understand and generate content across multiple formats, including text, images, and code. This versatility allows one model family to support email drafting, document summarization, image generation workflows, and software- development assistance across different departments. Gemini is not completely open-source, eliminating option A. Although Gemini can support the creation of AI agents, agent customization is not the defining benefit connecting all the tasks described. Specialized business-intelligence analysis is also narrower than the organization's cross-functional requirements. The question emphasizes several different content types and activities rather than one specialized workflow. Therefore, Gemini's ability to work with text, visual information, and code provides the broadest and most directly relevant advantage for improving organizational efficiency.
NEW QUESTION # 74
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. Data agent
- B. Code agent
- C. Customer service agent
- D. Security agent
Answer: D
Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
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NEW QUESTION # 75
A market research analyst needs a Google Cloud prebuilt generative AI tool to consistently generate weekly reports summarizing key trends and news from publicly available data sources in the technology industry.
They want the most efficient process, a consistent report each week covering the latest developments, and to avoid repeatedly specifying the desired industry and types of information to track. What should they do?
- A. Use the Gemini app to draft each weekly industry trend report from scratch with manual input.
- B. Use custom Gems in Gemini Advanced to save industry, trend/news types, and report formatting.
- C. Use Google Workspace Gemini in Docs for collaborative report building.
- D. Use NotebookLM to analyze past reports for themes and improvements.
Answer: B
Explanation:
A custom Gem enables the analyst to configure reusable instructions describing the technology industry, the trends and news categories to monitor, and the required weekly-report structure. Once configured, the Gem applies those directions consistently during subsequent interactions, removing the need to rewrite an extensive prompt every week. This supports both efficiency and standardized reporting while allowing Gemini to work with current publicly available information. NotebookLM is primarily grounded in sources uploaded or supplied to a notebook and would be more appropriate for analyzing a defined collection of documents.
Drafting from scratch in the Gemini app requires repeated manual prompting, which contradicts the efficiency requirement. Gemini in Docs can assist with writing and collaboration, but it does not by itself preserve a specialized, reusable persona and instruction set. A custom Gem is therefore the best fit.
NEW QUESTION # 76
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
- A. Applying data anonymization techniques to remove or obscure sensitive data.
- B. Focusing on collecting only quantitative feedback data in future surveys.
- C. Ensuring that the AI model is trained on a large and diverse dataset.
- D. Implementing strong access controls to limit which teams can view the raw survey data.
Answer: A
Explanation:
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google's Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model's parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application's outputs. This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
NEW QUESTION # 77
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