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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 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	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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?

A. Improved IT infrastructure management across offices.
B. Automation of employee performance reviews using AI.
C. Seamless knowledge sharing and collaboration across internal systems.
D. Enhanced data encryption and compliance for internal communications.

Answer: C

Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
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NEW QUESTION # 45
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?

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

Answer: B

Explanation:
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.
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NEW QUESTION # 46
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. Model Registry
B. Cloud Storage
C. Vertex AI Pipelines
D. BigQuery

Answer: A

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 # 47
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?

A. Integrating the AI application with various job boards to maximize candidate reach.
B. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
C. Ensuring that the AI application can automatically rank all candidates without requiring human review.
D. Focusing on minimizing the processing time for each application to improve efficiency.

Answer: B

Explanation:
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.
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NEW QUESTION # 48
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?

A. Use gen AI to identify potential bugs and security vulnerabilities in their code.
B. Use gen AI to suggest code snippets and complete functions.
C. Use gen AI to automatically generate comprehensive documentation for their code.
D. Use gen AI to refactor and optimize existing code.

Answer: B

Explanation:
While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping.
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NEW QUESTION # 49
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