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Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
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 pre-trained models and low- and no-code AI tools and services.
B. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
C. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
D. Google Cloud's comprehensive training materials and tutorials to help developers.

Answer: A

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 # 22
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. Document AI
C. Grounding with Google Search
D. Vertex AI Natural Language API

Answer: C

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 # 23
What is an example of unsupervised machine learning?

A. Forecasting sales figures using historical sales and marketing spend.
B. Training a system to recognize product images using labeled categories.
C. Analyzing customer purchase patterns to identify natural groupings.
D. Predicting subscription renewal based on past renewal status data.

Answer: C

Explanation:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
________________________________________

NEW QUESTION # 24
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 refactor and optimize existing code.
C. Use gen AI to suggest code snippets and complete functions.
D. Use gen AI to automatically generate comprehensive documentation for their code.

Answer: C

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.
________________________________________

NEW QUESTION # 25
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. Limit the chatbot's training data to prevent it from learning irrelevant information.
B. Lower model temperature setting to produce more consistent and predictable responses.
C. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
D. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.

Answer: D

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.
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NEW QUESTION # 26
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Exam Generative-AI-Leader Collection Pdf | Pass-Sure Google Generative-AI-Leader: Google Cloud Certified - Generative AI Leader Exam 100% Pass

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q21-Q26):

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