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Free DP-100 Exam Braindumps (page: 4)

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PDF with 512 Questions

You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.
You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).
Which of the following actions should you take?

  1. You should consider including Rattle.
  2. You should consider including TensorFlow.
  3. You should consider including Theano.
  4. You should consider including Chainer.

Answer(s): B

Explanation:


Reference:

https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html



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 have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.
You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.
Recommendation: You configure the use of the value k=3.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): B



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 have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.
You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.
Recommendation: You configure the use of the value k=10.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:

Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.
LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.
This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.



You construct a machine learning experiment via Azure Machine Learning Studio.
You would like to split data into two separate datasets.
Which of the following actions should you take?

  1. You should make use of the Split Data module.
  2. You should make use of the Group Categorical Values module.
  3. You should make use of the Clip Values module.
  4. You should make use of the Group Data into Bins module.

Answer(s): A

Explanation:

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins






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