Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 111:

  HOTSPOT

  You are analyzing the asymmetry in a statistical distribution.

  The following image contains two density curves that show the probability distribution of two datasets.

  

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

  NOTE: Each correct selection is worth one point.

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  Correct Answer:

  

  Box 1: Positive skew

  Positive skew values means the distribution is skewed to the right.

  Box 2: Negative skew

  Negative skewness values mean the distribution is skewed to the left.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics

• Question 112:

  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:

  

  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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  Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data

  The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).

  Box 2: Penalty parameter

  Parameter: C : float, optional (default=1.0)

  Penalty parameter C of the error term.

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

  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:

  

• Question 113:

  You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.

  You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.

  You create the following Python data frames:

  

  You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets. 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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  Box 1: PCA(n_components = 10)

  Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.

  Example:

  from sklearn.decomposition import PCA

  pca = PCA(n_components=2) ;2 dimensions

  principalComponents = pca.fit_transform(x)

  Box 2: pca

  fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.

  Box 3: transform(x_test)

  transform(X) applies dimensionality reduction to X.

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

• Question 114:

  HOTSPOT

  You are performing sentiment analysis using a CSV file that includes 12.0O0 customer reviews written in a short sentence format.

  You add the CSV file to Azure Machine Learning Studio and Configure it as the starting point dataset of an experiment.

  You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

  You must create a new n-gram text dictionary from the customer review text and set the maximum n-gram size to trigrams.

  You need to configure the Extract N Gram features from Text module.

  What should you select? To answer, select the appropriate options in the answer area;

  NOTE: Each correct selection is worth one point.

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  Correct Answer:

  

• Question 115:

  HOTSPOT

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

  You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.

  You need to create the required business metrics.

  How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area;

  NOTE: Each correct selection is worth one point.

  

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  Correct Answer:

  

• Question 116:

  HOTSPOT

  You need to set up the Permutation Feature Importance module according to the model training requirements.

  

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

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  Box 1: Accuracy

  Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval,

  thereby directing effort and resources towards models that are more likely to be successful.

  Box 2: R-Squared

• Question 117:

  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:

  

• Question 118:

  HOTSPOT

  You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.

  You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:

  1.

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

  2.

  Batch size must be 16, 32 and 64.

  3.

  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 param_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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  In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.

  Example:

  from azureml.train.hyperdrive import RandomParameterSampling

  param_sampling = RandomParameterSampling( { "learning_rate": normal(10, 3), "keep_probability": uniform(0.05, 0.1), "batch_size": choice(16, 32, 64) }

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

• Question 119:

  HOTSPOT

  You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.

  You must meet the following requirements:

  1.

  Reduce the number of training epochs.

  2.

  Reduce the size of the neural network.

  3.

  Reduce over-fitting of the neural network.

  You need to select the image modification values.

  Which value 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:

  

• Question 120:

  HOTSPOT

  You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer: