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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A large company is creating their generative AI (gen AI) solution by using Google Cloud's offerings. They want to ensure that their mid-level managers contribute to a successful gen AI rollout by following Google-recommended practices. What should the mid-level managers do?

• A. Create a robust data strategy to ensure teams can access high-quality, relevant data that is appropriate for training and fine-tuning gen AI models.
• B. Secure funding and resources for AI initiatives by demonstrating the potential return on investment to the chief financial officer (CFO).
• C. Perform continuous testing, measurement, and refinement based on user feedback and real-world performance data.
• D. Drive gen AI adoption by identifying high-impact, feasible solutions that address specific challenges within their workflows.

Answer: D

Explanation:
Google's recommended strategy for a successful generative AI rollout involves a combination of top-down strategic alignment and bottom-up adoption. In this structure, the role of the mid-level manager is critical for driving tangible value within their specific domain.
Securing funding (D) is typically the responsibility of senior leadership or the steering committee.
Creating a robust data strategy (B) is the domain of data governance teams and data scientists.
Continuous testing and refinement (A) is the job of MLOps/engineering teams and end-users.
The primary role of the mid-level manager is to act as the bridge between high-level strategy and daily operations. They possess the domain knowledge to pinpoint pain points. Therefore, their most impactful contribution is to identify specific, high-impact, and feasible use cases (C) for their teams-such as automating report summaries or drafting internal communications-that directly address operational challenges and demonstrate quick wins. This action fuels successful adoption and validates the AI strategy from the ground up.
(Reference: Google Cloud's guidance on Gen AI strategy emphasizes that successful adoption requires strong top-down vision (like defining goals/funding) combined with bottom-up discovery, where functional leaders (mid-level managers) identify and prioritize high-value, feasible solutions within their specific workflows to drive adoption.)

 

NEW QUESTION # 13
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time- consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?

• A. Document AI API
• B. Dataflow
• C. Vision AI
• D. Natural Language API

Answer: A

Explanation:
Document AI API is specifically designed for intelligent document processing. It uses machine learning to extract structured data from unstructured documents like scanned forms and PDFs, even with varying layouts.
This directly addresses the challenge of automating data extraction from loan applications. Natural Language API focuses on text understanding, Vision AI on image analysis (not structured extraction from documents), and Dataflow is for data processing pipelines.
________________________________________

 

NEW QUESTION # 14
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?

• A. Identity and Access Management
• B. Workload monitoring tools
• C. Security Command Center
• D. Secure-by-design infrastructure

Answer: A

Explanation:
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
________________________________________

 

NEW QUESTION # 15
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 fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
• B. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
• C. RAG uses human oversight to ensure accuracy before presenting information to the customer.
• D. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.

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.
________________________________________

 

NEW QUESTION # 16
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?

• A. Enhanced real-time communication and collaboration among team members.
• B. Greater interoperability with legacy software systems and databases.
• C. Improved productivity and data interaction using AI assistants and advanced document analysis.
• D. More granular control over support team access and permissions for sensitive data.

Answer: C

Explanation:
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.

 

NEW QUESTION # 17
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