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Pass Guaranteed 2025 Generative-AI-Leader: Google Cloud Certified - Generative AI Leader Exam Perfect Latest Real Test

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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

• Servicing AV Solutions: This section of the exam measures skills of an AV Maintenance Technician and focuses on maintaining and repairing AV systems. Tasks include supervising operations, conducting routine maintenance such as firmware updates and component replacements, and resolving issues through troubleshooting and repair processes, ensuring long-term system performance.

Topic 2

• Supporting AV System Operation: This section of the exam measures the skills of an AV Support Specialist and focuses on providing assistance in operating audiovisual systems. It includes remote and onsite troubleshooting, training users, and delivering live event support, ensuring systems function effectively in real-world usage scenarios.

Topic 3

• Implementing AV Solutions: This section of the exam measures the skills of an AV Integration Technician and focuses on turning AV system designs into reality. It assesses the ability to integrate systems by verifying components and managing supply installations, as well as handling documentation, training, and creating as-built schematics that support ongoing system operation.

Topic 4

• Creating AV Solutions: This section of the exam measures the skills of an AV Systems Designer and covers the processes involved in understanding client requirements and translating them into practical AV solutions. It includes tasks such as conducting customer needs analysis, performing site surveys to evaluate conditions like lighting and acoustics, developing AV project scopes, and designing system layouts and documentation.

 

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

Answer: B

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.
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NEW QUESTION # 11
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

• A. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
• B. Gemini is not equipped to handle structured numerical data for financial assessments.
• C. Gemini deployment for this scenario would be too expensive and complex.
• D. Gemini cannot integrate with required financial databases.

Answer: A

Explanation:
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.
________________________________________

 

NEW QUESTION # 12
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human- like conversations and provide accurate information. What should they do to enhance thechatbot's ability to understand and respond effectively to user prompts?

• A. Lower model temperature setting to produce more consistent and predictable responses.
• B. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
• C. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
• D. Limit the chatbot's training data to prevent it from learning irrelevant information.

Answer: C

Explanation:
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input- output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human- like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
________________________________________

 

NEW QUESTION # 13
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?

• A. Use role prompting.
• B. Use prompt chaining.
• C. Adjust the temperature parameter.
• D. Use few-shot prompting.

Answer: A

Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
________________________________________

 

NEW QUESTION # 14
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. Deep learning
• B. Unsupervised 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.
________________________________________

 

NEW QUESTION # 15
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