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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q28-Q33):
NEW QUESTION # 28
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. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
- B. Ensuring that the AI application can automatically rank all candidates without requiring human review.
- C. Focusing on minimizing the processing time for each application to improve efficiency.
- D. Integrating the AI application with various job boards to maximize candidate reach.
Answer: A
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 # 29
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. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
- B. Lower model temperature setting to produce more consistent and predictable responses.
- C. Limit the chatbot's training data to prevent it from learning irrelevant information.
- 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 # 30
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?
- A. Gemini
- B. Chirp
- C. Imagen
- D. CodeGemma
Answer: A
Explanation:
Gemini models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
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NEW QUESTION # 31
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?
- A. A customized learning agent
- B. A large language model fine-tuned on educational content
- C. An AI-powered recommendation system for learning resources
- D. A learning management system (LMS)
Answer: A
Explanation:
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
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NEW QUESTION # 32
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?
- A. Google Cloud Storage
- B. BigQuery
- C. Google Kubernetes Engine (GKE)
- D. Vertex AI
Answer: D
Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
________________________________________
NEW QUESTION # 33
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