[Free] 2019(Nov) EnsurePass Microsoft DP-100 Dumps with VCE and PDF 21-30

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Question No.21

HOTSPOT

You are using the Azure Machine Learning Service to automate hyper par a meter exploration of your neural network classification model.

You must define the hyper parameter space to automatically tune hyper parameters using random sampling according to following requirements:

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Learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.

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Batch size must be 16, 32 and 64.

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Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.

You need to use the par am .sampling method of the Python API for the Azure Machine Learning Service.

How should you complete the code segment? To answer, select the appropriate Options in the answer area.

NOTE: Each correct selection is worth one point.

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Correct Answer:

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Question No.22

HOTSPOT

You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

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Correct Answer:

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Question No.23

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column. Solution: Apply a Quantiles binning mode with a PQuantile normalization.

Does the solution meet the goal?

  1. Yes

  2. No

Correct Answer: B

Explanation:

Use the Entropy MDL binning mode which has a target column.

References:

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

Question No.24

HOTSPOT

You are using C-Support Vector classification to do a multi-class classification with an

unbalanced training dataset. The C-Support Vector classification using Python code shown below:

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You need to evaluate the C-Support Vector classification code.

Which evaluation statement should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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Correct Answer:

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Question No.25

HOTSPOT

You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure. You need to access the DSVMs.

Which utilities should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

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Question No.26

HOTSPOT

You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.

The following image displays the results dataset output:

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Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.

NOTE: Each correct selection is worth one point.

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Correct Answer:

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Question No.27

You create a binary classification model by using Azure Machine Learning Studio.

You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:

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iterate all possible combinations of hyperparameters minimize computing resources required to perform the sweep

You need to perform a parameter sweep of the model. Which parameter sweep mode should you use?

  1. Random sweep

  2. Sweep clustering

  3. Entire grid

  4. Random grid

  5. Random seed

Correct Answer: 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.

Question No.28

You are creating a machine learning model. You have a dataset that contains null rows.

You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.

Which parameter should you use?

  1. Replace with mean

  2. Remove entire column

  3. Remove entire row

  4. Hot Deck

Correct Answer: B

Explanation:

Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing- data

Question No.29

You are solving a classification task. The dataset is imbalanced.

You need to select an Azure Machine Learning Studio module to improve the classification accuracy.

Which module should you use?

  1. Fisher Linear Discriminant Analysis.

  2. Filter Based Feature Selection

  3. Synthetic Minority Oversampling Teachnique (SMOTE)

  4. Permutation Feature Importance

Correct Answer: A

Question No.30

You are performing a filter based feature selection for a dataset 10 build a multi class classifies

by using Azure Machine Learning Studio.

The dataset contains categorical features that are highly correlated to the output label column.

You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?

  1. Chi-squared

  2. Spearman correlation

  3. Kendall correlation

  4. Person correlation

Correct Answer: A

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