ExamPark
Course menuAIF-C01

Exams / AIF-C01 / Bias, variance and datasets / Question 1

AIF-C01 practice question: Bias, variance and datasets (1 of 2)

Guidelines for Responsible AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

Last verified against current AWS documentation ·How we write our questions

A logistics company's model predicts delivery delays. It scores 98 percent on its training data but 71 percent on a held-out test set.

The team cannot collect new data this quarter and must keep the model's size unchanged to meet a latency limit.

Which action is MOST likely to improve performance on new data?

Choose one answer.

  1. A. Apply regularization and early stopping, and tune them on a validation set kept separate from the test set.
  2. B. Train for many more epochs, so that the model fits the training data even more closely.
  3. C. Merge the validation and test sets, then retrain on all of the data so the model sees more examples.
  4. D. Add more hidden layers, so that the network can learn more complex patterns in the training data.
Show the answer and explanation

Answer: A

  • A. Right: Correct. Regularization and early stopping limit how closely the model fits noise in the training data. A separate validation set keeps the tuning honest, so the test set still measures new-data performance. Learn more: What is Overfitting? - AWS (opens in a new tab)
  • B. Wrong: More epochs usually widen the gap between training and new-data accuracy. The model memorizes the training examples even further.
  • C. Wrong: Merging the sets removes the only unbiased check on new-data accuracy. Retraining on the same data does not fix overfitting, which is the real problem.
  • D. Wrong: A larger network increases model size, which breaks the latency limit. More capacity also tends to overfit more, so it works against the goal.

Explanation

A large gap between training and test accuracy is a sign of overfitting, where a model learns the training data too closely to generalize. Regularization and early stopping address this without new data or a larger model, and a clean validation process keeps the evaluation honest. This question tests recognizing overfitting and choosing a fix that respects the constraints.

Study

Keep practicing

Answer questions with a score at the end, or practice just this topic with the explanation after each one. No sign-up needed.

Take the free AIF-C01 practice examPractice Bias, variance and datasetsAll Bias, variance and datasets questions

Want more on Bias, variance and datasets? The 5 full-length practice sets have 9 more Bias, variance and datasets questions, 325 in all, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets