TensorRT uye Inference Injini Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

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. layer fusion inokurumidza sei kufungidzira?
   • Nekubatanidza mashandiro akawanda muGPU kernel kudzikisa ndangariro traffic
   • Nekudzima mitsara isingadikanwi
   • Nekumhanyisa yega yega paGPU yakaparadzana
   • Nekushandura modhi kuti imhanye paCPU
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. Nei injini yeTensorRT yakasanganiswa kazhinji yakananga kune imwe GPU yekuvakisa?
   • Nekuti yakavharirwa kune iyo GPU's serial nhamba
   • Nekuti NVIDIA inopa marezinesi pamudziyo wega wega
   • Nekuti inochengeta data rekudzidziswa mukati meinjini
   • Nekuti kernel auto-tuning inobika mumakernels anokurumidza kune iyo GPU chaiyo musimboti uye ndangariro dhizaini.
5. Ndechipi chimiro cheTensorRT-LLM chinonyatso batsira mitauro mikuru nemazvo?
   • Mundege (inoenderera) batching uye peji KV-cache
   • Otomatiki mufananidzo kuwedzera
   • Yakavakwa-mukati webhu saiti
   • Audio ruzha kukanzura
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