AI ٹریننگ کوئز

جب آپ کوئز دیتے ہیں تو جوابات اور وضاحتیں ظاہر ہوتی ہیں۔

1. کون سی ٹریننگ لاگ انٹری ایک مکمل دور کی وضاحت کرتی ہے؟
   • A full pass through the training dataset
   • One optimizer update from a single example
   • A test performed after deployment
   • A record of one model prediction
2. کون سی ترتیب ایک بنیادی زیر نگرانی تربیتی قدم کو بیان کرتی ہے؟
   • The test set updates weights, then training labels are generated
   • The model writes a target answer, then deletes the input
   • The model predicts, loss compares with a target, and an optimizer updates parameters
   • A prompt changes weights and the loss is ignored
3. Validation has selected a training configuration. Why evaluate that model on a separate test set next?
   • To use its labels for every weight update
   • To evaluate the selected model on data not used to fit or choose it
   • To make the training loss smaller
   • To store preprocessing rules
4. ڈیٹا لیک ہونے والی معلومات کو کیسے تقسیم کیا جا سکتا ہے؟
   • All records from one source are kept together
   • Later periods are used only for later forecasts
   • Preprocessing rules are stored with the model
   • Related records from the same person or event are split across train and test
5. کیا پہلے سے تربیت یافتہ ماڈل میں پرامپٹ ٹائپ کرنا ضروری طور پر اس کی تربیت کرتا ہے؟
   • No; a prompt changes the current context, while weight updates are a separate process
   • Yes; every prompt immediately updates model parameters
   • Yes, but only if the answer is long
   • No; prompts are ignored by trained models
6. تولیدی صلاحیت کو سہارا دینے کے لیے تربیتی چوکی کے ساتھ کیا بچایا جانا چاہیے؟
   • Only the final model filename
   • Only the training loss
   • Data version, preprocessing rules, model configuration, and evaluation results
   • The test labels mixed into training
7. Why does the training objective matter?
   • It guarantees the model learns the outcome that users care about
   • It determines what the model is rewarded for learning
   • It is the same as the model’s deployment policy
   • It removes the need for test data
8. What does a lower training loss establish?
   • That the model will generalize to all new examples
   • That the labels are factually correct
   • That deployment monitoring is unnecessary
   • Better agreement with the training objective, not reliability on every future input