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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
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. Rapid implementation strategy
B. Top-down strategy
C. Multi-directional strategy
D. Bottom-up strategy

Answer: B

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.
________________________________________

NEW QUESTION # 36
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 enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
B. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
C. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
D. RAG uses human oversight to ensure accuracy before presenting information to the customer.

Answer: A

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 # 37
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 # 38
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. Security agent
B. Code agent
C. Data agent
D. Customer service agent

Answer: A

Explanation:
Given the tasks

NEW QUESTION # 39
What will Google Cloud's Agent Assist help a company achieve?

A. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
B. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
C. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.
D. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.

Answer: D

Explanation:
Google Cloud's Agent Assist is specifically designed to augment human customer service agents. It provides real-time suggestions, retrieves relevant information, and offers recommended responses to agents during live interactions, improving their efficiency and consistency.
________________________________________

NEW QUESTION # 40
......

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