TensorRT ak quiz ci motëri jàngat

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. What is the goal of building an inference engine with TensorRT?
   • Guarantee the fastest possible execution on every system
   • Optimize execution of a supported trained model for the configured target
   • Choose training labels without a dataset
   • Replace every GPU with a CPU
2. naka la fusion couche di gaawaalee inférence bi?
   • Soo boole ay liggéey yu bari ci benn kernel GPU ngir wàññi dem bi ak dikk bi ci mémoire bi
   • Sooy efaase ay diisaay yuñ soxlawul
   • Sooy doxal layer bu nekk ci GPU bu wuute
   • Soo soppi model bi mu dox ci CPU bi
3. In a representative-data INT8 calibration workflow, why examine the model’s value distributions?
   • To guarantee no accuracy change under all future inputs
   • To retrain the model from zero automatically
   • To select quantization scales suited to the observed numerical ranges
   • To remove every compatibility requirement
4. Lan moo waral benn motër TensorRT buñ dajale dafay nekk benn architecture GPU kese?
   • Ndax dañu ko encrypté ci nimero serie GPU bi
   • Ndax NVIDIA daf koy jox lisence ci aparey bu nekk
   • Ndax dafay denc done yiñ tàggat ci biir motër bi
   • Ndax kernel bi dafay aju ci kernel yi gëna gaaw ci GPU bi gëna jub ci core bi ak ci jëmmu mémoire bi
5. Ban màndarga ci TensorRT-LLM mooy gëna jàppale modeli làkk yu mag yi ci anam wu jaar yoon?
   • ci biir naaw (di wéy) batching ak paged KV-cache
   • Yokkateg nataal ci saa si
   • Web hosting buñ tabax ci biir
   • fomm bruit audio
6. Why evaluate task accuracy after choosing lower-precision TensorRT execution?
   • Lower precision always preserves every output bit
   • Only the model filename affects predictions
   • Reduced numerical precision can change model outputs or quality
   • Accuracy evaluation is needed only during tokenization
7. A TensorRT dynamic-shape engine receives input outside its configured profile range. What should deployment validation check?
   • Whether the engine can accept every shape without a profile
   • Only whether the model name is short
   • Whether training can restart from a screenshot
   • Whether the profile supports that requested shape
8. A TensorRT engine is moved to a different runtime and GPU configuration. What must the team establish?
   • That any serialized engine runs unchanged everywhere
   • That the selected compatibility settings support the new environment
   • That the engine contains all its original training data
   • That every GPU shares one serial number