Prompt Engineering and Augmentation GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A customer service bot for a software company is asked: 'How do I reset my password?' The baseline LLM gives a generic answer that works for most websites, but the company has a specific process that includes verifying identity via email. How can augmentation improve the response?

   • AAdd an instruction to 'be helpful' without any additional context.
   • BRetrieve the company's password reset procedure and include it in the prompt as context.
   • CRemove the word 'password' from the prompt to avoid confusion.
   • DUse the baseline response because it is generally correct.
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2. 2foundation · easy

   What is the primary goal of iterative refinement in prompt engineering?

   • ATo ensure the model never makes a mistake.
   • BTo systematically improve the prompt based on observed outputs until the desired response is achieved.
   • CTo reduce the number of tokens in the prompt to the minimum possible.
   • DTo make the prompt as complex as possible.
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4. 3application · medium

   A support chatbot for a telecom company receives: 'My internet has been down since yesterday and I need it fixed today because I work from home.' The team wants to augment the prompt with structured context. Which augmentation should be applied first?

   • AIdentify the intent as 'billing inquiry' and extract 'yesterday' as the only key field.
   • BUse the raw user input as the augmentation context without any extraction or intent detection.
   • CExtract only the term 'internet' and add it to the prompt as a keyword to improve retrieval.
   • DExtract the intent as 'outage report' and the key fields 'internet', 'down since yesterday', and 'work from home' to build a structured context block.
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5. 4foundation · easy

   Why is intent recognition important for prompt augmentation?

   • AIt helps the system decide what kind of additional context to retrieve or include in the prompt.
   • BIt ensures the LLM always responds in a specific format.
   • CIt replaces the need for key field extraction.
   • DIt reduces the number of tokens in the prompt.
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6. 5foundation · easy

   What is the main difference between key field extraction and term extraction?

   • AKey field extraction is used for short inputs, while term extraction is used for long inputs.
   • BKey field extraction is performed by the LLM, while term extraction is performed by a separate model.
   • CKey field extraction focuses on structured data points (e.g., dates, numbers), while term extraction focuses on important domain-specific words or phrases.
   • DKey field extraction produces a summary, while term extraction produces a list of keywords.
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