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Free DP-100 Exam Braindumps (page: 11)

Page 10 of 127
PDF with 512 Questions

You make use of Azure Machine Learning Studio to create a binary classification model.
You are preparing to carry out a parameter sweep of the model to tune hyperparameters. You have to make sure that the sweep allows for every possible combination of hyperparameters to be iterated. Also, the computing resources needed to carry out the sweep must be reduced.
Which of the following actions should you take?

  1. You should consider making use of the Selective grid sweep mode.
  2. You should consider making use of the Measured grid sweep mode.
  3. You should consider making use of the Entire grid sweep mode.
  4. You should consider making use of the Random grid sweep mode.

Answer(s): D

Explanation:

Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters



You are in the process of constructing a deep convolutional neural network (CNN). The CNN will be used for image classification.
You notice that the CNN model you constructed displays hints of overfitting.
You want to make sure that overfitting is minimized, and that the model is converged to an optimal fit.
Which of the following is TRUE with regards to achieving your goal?

  1. You have to add an additional dense layer with 512 input units, and reduce the amount of training data.
  2. You have to add L1/L2 regularization, and reduce the amount of training data.
  3. You have to reduce the amount of training data and make use of training data augmentation.
  4. You have to add L1/L2 regularization, and make use of training data augmentation.
  5. You have to add an additional dense layer with 512 input units, and add L1/L2 regularization.

Answer(s): D

Explanation:

B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
- L1: Sum of the absolute weights.
- L2: Sum of the squared weights.
- L1L2: Sum of the absolute and the squared weights.
Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.


Reference:

https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://en.wikipedia.org/wiki/Convolutional_neural_network



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Attached compute.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): B

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Inference cluster.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): B

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio






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