1.3 Explain Role of Context Windows, Token Limits and Response Management AI-TECHNICAL-PRACTITIONER Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   In the context of token limits, what does a 'token' most commonly represent in a large language model?

   • AA single character
   • BA byte of data
   • CA word or subword unit
   • DA sentence
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2. 12foundation · easy

   A model has a token limit of 4,096 tokens. If the input prompt uses 3,000 tokens, how many tokens are available for the model's response?

   • A4,096 tokens
   • B3,000 tokens
   • C7,096 tokens
   • D1,096 tokens
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4. 13application · medium

   A support team is building a chatbot that answers questions about a 200-page product manual. They want the model to reference the entire manual when generating answers, but the model's context window is 8,000 tokens. Which approach best allows the chatbot to answer accurately while staying within the token limit?

   • AIncrease the model's context window by adjusting the temperature setting.
   • BSummarize the entire manual into a single paragraph and include that summary in every prompt.
   • CSplit the manual into sections, retrieve the most relevant section for each query, and include only that section in the prompt.
   • DSend the entire manual as a single prompt and rely on the model to truncate its response.
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5. 14application · medium

   A company is using a language model to answer customer support questions. The model's context window is 3,000 tokens, and the knowledge base articles are each about 2,500 tokens. The team wants to include the most relevant article in each prompt. What is the best way to do this?

   • AUse a larger context window by changing the temperature setting.
   • BInclude the entire article and let the model truncate the response.
   • CSummarize each article to a shorter version before including it in the prompt.
   • DSend the article in a separate API call and then ask the model to answer based on the response.
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6. 15application · medium

   A developer is testing a language model and notices that when the input is very long, the model's response quality decreases. What is the most likely reason?

   • AThe input is approaching the token limit, so the model may truncate or lose important information.
   • BThe model is running out of memory and cannot process the entire input.
   • CThe temperature setting is too high, causing the model to produce lower-quality responses.
   • DThe max_tokens parameter is set too low, limiting the response length.
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