NLP Temelleri Testi

Testi çözdüğünüzde cevaplar ve açıklamalar görünür.

1. An application must assign one category to each whole customer message. Which NLP output type fits that task?
   • A document-level classification label
   • A sequence of new translated sentences
   • Character spans marking names only
   • A transcript of audio that was never supplied
2. In the guide’s sentence “Mina at Northstar Labs said the delayed launch was disappointing,” what could an entity-recognition task mark?
   • The overall sentiment as a single document label
   • Mina as a person and Northstar Labs as an organization
   • Fransızcaya bir çeviri
   • A summary claiming why the launch was delayed
3. Why might a tokenizer split a written word into more than one token?
   • Token boundaries always match syllables exactly
   • It must first translate every input
   • Token units depend on the tokenizer and can include word pieces
   • Every tokenizer removes all punctuation before processing
4. For “the delayed launch was disappointing,” which output best matches a sentiment task?
   • A span marking “launch” as an organization
   • Başka bir dile çeviri
   • A list of the sentence’s tokens only
   • A label describing the expressed reaction as negative
5. A text classifier fails on sarcasm and negation common in real user input. What should the team do?
   • Build evaluation examples reflecting language conditions the application encounters
   • Remove context so every phrase is isolated
   • Assume tidy benchmark accuracy covers every style
   • Ask the model to label its own errors without checking
6. Why keep an original passage beside information extracted by an NLP system?
   • To guarantee the extraction is correct without review
   • So a reader can confirm the extracted result in context
   • To convert named entities into new model parameters
   • To remove the need to evaluate the application
7. A long document is silently truncated before processing. What is the main risk named by the guide?
   • The model will automatically recover every omitted paragraph
   • The text becomes a structured database
   • Important sections may be removed from the input
   • Truncation changes the author of the document
8. A team has a narrow text-routing task. What comparison does the guide recommend considering?
   • Only the largest language model available
   • A model that ignores the task definition
   • A model output without any evaluation examples
   • A simple rule or keyword baseline alongside more complex systems