AI Models Inotsanangurwa Mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi chikamu cheAI application ndiyo yemuchina-yekudzidza modhi pachayo?
   • Iyo masvomhu inoronga zvinopinza kune zvakabuda zvine chimiro uye zvakadzidzwa paramita
   • Mushandisi interface ine zvese zvechigadzirwa chimiro
   • Seti yemawebhusaiti iyo modhi inotenderwa kushanyira
   • Gwaro remitemo rinotsanangura mashandisiro anoita timu AI
2. Chikwata chine muganho wakasimba we100 ms latency. Muyedzo yegwara remufananidzo, ndeupi mumiriri anosangana nayo?
   • Model B at 94% accuracy and 400 ms
   • Model A at 92% accuracy and 30 ms
   • Neither, because accuracy must be 100%
   • Both, because they score above 90%
3. Ndechipi chinhu chiri hyperparameter pane kudzidza parameter?
   • A weight value adjusted during training
   • A coefficient the fitted model uses for an input
   • A tree-depth limit selected through validation
   • A prediction returned for one example
4. Iyo yekutsvaga system inopa gwaro rakadzoserwa kune modhi panguva yekubvunza. Chii chinochinja pasina kudzidziswazve?
   • It necessarily retrains model weights
   • It fine-tunes the model on every query
   • It permanently changes the model version
   • It adds available evidence without changing model weights
5. Mugadziri anonatsa modhi pane mimwe mienzaniso. Chii chinochinja?
   • Model weights, without guaranteeing the information is current
   • Only the text retrieved for one query
   • The application’s user interface but never the model
   • The validation set but not model behavior
6. A team adapts a foundation model to a new document task. Which statement about this reuse is accurate?
   • It is necessarily the first AI model ever built
   • It can be adapted to multiple tasks, but still needs evaluation
   • It is only a model for building construction
   • It must be fine-tuned before it can answer any prompt
7. Why should a team re-evaluate after changing a model version?
   • Interfaces always change whenever versions change
   • A new version makes latency measurements unnecessary
   • Behavior can change even while interfaces stay stable
   • A model name proves it is more accurate
8. How should a team choose among candidate models for a defined application?
   • Select the largest parameter count by default
   • Use a leaderboard score without checking its errors
   • Choose the newest version without testing
   • Compare representative inputs, errors and operating constraints, then prefer the simplest adequate option