Foundational Concepts DY0-001 Practice Questions (Page 4)

Questions 16–20

1. 16application · easy

   A data scientist is training a neural network for a multi-class classification problem with 10 classes. The model's output layer uses a softmax activation function. Which loss function is most appropriate to use with this output layer?

   • ACategorical cross-entropy
   • BMean squared error (MSE)
   • CBinary cross-entropy
   • DHinge loss
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What is the main purpose of cross-validation in machine learning?

   • ATo reduce the number of features in the dataset
   • BTo ensure the model is trained on all available data
   • CTo assess how well a model generalizes to unseen data
   • DTo increase the size of the training dataset
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   What is the primary purpose of regularization techniques in machine learning?

   • ATo prevent overfitting by penalizing large model parameters
   • BTo reduce the size of the training dataset
   • CTo increase model complexity and improve training accuracy
   • DTo speed up the training process
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   What is the primary goal of ensemble methods in machine learning?

   • ATo simplify the model architecture
   • BTo combine multiple models to improve overall prediction accuracy and robustness
   • CTo automatically select the best hyperparameters
   • DTo reduce the training time of a single model
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   Which of the following is an example of a hyperparameter?

   • AThe weights learned by a neural network during training
   • BThe bias term in a linear regression model
   • CThe predicted class labels for test samples
   • DThe learning rate of a gradient descent optimizer
   Reveal Solution Discussion Select an answer first