MLS-C01 exam dumps: questions 201 to 300 of 388

Correct answer: B

• A. Incorrect.

  Random initialization with values between 0 and 1 is not ideal as it can lead to vanishing or exploding gradients, especially in deep networks. It lacks the mathematical foundation to ensure gradients flow properly through the network.

• B. Correct.

  Xavier (Glorot) initialization is specifically designed to balance the variance of weights across layers in deep networks. It helps to prevent vanishing or exploding gradients, making it a suitable choice for initializing weights in image classification models.

• C. Incorrect.

  Constant initialization with all weights set to zero is not recommended as it causes symmetry problems during training, leading to ineffective model learning.

• D. Incorrect.

  He initialization is optimized for activation functions like ReLU and its variants. While it's effective for certain tasks, Xavier initialization is generally more suitable for image classification tasks where the activation functions might not always be ReLU-based.

Correct answers: A, C

• A. Correct.

  Correct. SageMaker allows you to provide pre-trained model artifacts stored in an S3 bucket to initialize the model with custom weights.

• B. Incorrect.

  Incorrect. SageMaker built-in algorithms do not have a generic 'initialWeights' hyperparameter. Custom weight initialization must be handled differently.

• C. Correct.

  Correct. Using frameworks like TensorFlow or PyTorch, you can load custom weights in your training script to initialize the model.

• D. Incorrect.

  Incorrect. SageMaker AutoPilot automatically builds and trains models but does not allow direct initialization of weights with custom values.

Correct answers: A, B, E

• A. Correct.

  Correct. Using pre-trained weights can help the model start training with a better baseline, improving convergence.

• B. Correct.

  Correct. Proven initialization methods like Xavier or He are specifically designed to improve convergence during training by addressing issues like vanishing or exploding gradients.

• C. Incorrect.

  Incorrect. Increasing the learning rate is not a direct solution to poor weight initialization and can lead to unstable training.

• D. Incorrect.

  Incorrect. Zero-initialization for all weights can lead to symmetry issues in the model, where all neurons in a layer learn the same features, hindering convergence.

• E. Correct.

  Correct. Ensuring that your network architecture is compatible with the chosen initialization method is critical, as certain initialization methods may be better suited for specific network types.

Correct answer: C

• A. Incorrect.

  The learning rate being too high would typically cause instability during training, but in this case, the learning rate is relatively small (0.001).

• B. Incorrect.

  ReLU activation functions do not cause vanishing gradients; they are designed to mitigate this issue compared to older activation functions like sigmoid or tanh.

• C. Correct.

  A low learning rate of 0.001 can result in the model learning too slowly, which is a common cause of non-convergence in deep learning models.

• D. Incorrect.

  While a large number of nodes in dense layers could lead to overfitting, it does not directly explain the lack of convergence during training.

Correct answers: B, E

• A. Incorrect.

  Increasing the learning rate may lead to instability in training and cause the model to overshoot the optimal solution. This is not a recommended action to improve convergence in this scenario.

• B. Correct.

  Switching to the ReLU activation function is a common approach to address slow convergence in deep neural networks because it helps mitigate the vanishing gradient problem often encountered with other activation functions like sigmoid or tanh.

• C. Incorrect.

  Decreasing the learning rate can slow down training even further. While it may help avoid overshooting in some cases, it does not directly address the issue of slow convergence.

• D. Incorrect.

  The sigmoid activation function is not suitable for a multi-class classification problem. For such tasks, using the softmax activation function in the output layer is more appropriate.

• E. Correct.

  Enabling learning rate scheduling allows the learning rate to adjust dynamically, which helps improve convergence by starting with a higher learning rate and gradually reducing it as the model approaches the optimal solution.

• F. Incorrect.

  Adding more layers might increase model capacity, but it does not directly address the issue of slow convergence and could lead to overfitting if not managed properly.