3.3 Identify Grounding Techniques and Their Use Cases. GENERATIVE-AI-LEADER Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Why is grounding used in LLM applications that provide customer support?

   • ATo allow the model to generate responses without any external data
   • BTo reduce the need for human oversight in customer interactions
   • CTo make the model respond faster to customer queries
   • DTo ensure responses reflect the latest product information and policies
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2. 7foundation · easy

   Which of the following is a typical use case for Google Cloud RAG APIs?

   • AGenerating random text without any external context
   • BTranslating languages using a pre-trained model
   • CBuilding a custom document Q&A system that retrieves from a company's private documents
   • DDeploying a model to production with autoscaling
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4. 8foundation · easy

   In which scenario is Google Cloud's Agent Search most useful?

   • AWhen a user needs to generate creative writing without external references
   • BWhen a developer wants to fine-tune a model on public web data
   • CWhen a company needs to ground a chatbot with its internal knowledge base
   • DWhen a team wants to build a custom RAG pipeline from scratch
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5. 9application · easy

   A social media company is deploying a generative AI assistant that interacts with users of all ages. The compliance team requires that the assistant refuse to generate content that is sexually explicit or promotes self-harm. Which configuration should the team apply?

   • ALower the temperature to 0.0.
   • BReduce the max output token count.
   • CUse RAG to ground the model in a safe-content database.
   • DConfigure safety settings to block the relevant content categories.
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6. 10application · easy

   A news aggregation app wants to add a feature where users can ask questions about breaking news stories from the past 24 hours. The app has no internal news archive. Which grounding approach best meets the requirement for current, web-sourced information?

   • AGrounding with the app's first-party user interaction data.
   • BGrounding with a static third-party news dataset from last year.
   • CGrounding with Google Search to retrieve recent articles and web content.
   • DRelying on the model's training data without additional grounding.
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