[New Exams!]100% Real Exam Questions-Braindump2go DP-100 Dumps 112Q Download[Q56-Q66]

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Question: 56

You are analyzing a dataset by using Azure Machine Learning Studio.
YOU need to generate a statistical summary that contains the p value and the unique value count for each feature column.
Which two modules can you users? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Execute Python Script
B. Export Count Table
C. Convert to Indicator Values
D. Summarize Data
E. Compute linear Correlation

Question: 57

You are building a binary classification model by using a supplied training set. The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.

A. Penalize the classification
B. Resample the data set using under sampling or oversampling
C. Generate synthetic samples in the minority class.
D. Use accuracy as the evaluation metric of the model.
E. Normalize the training feature set.

Question: 58

You are building recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.
Which of the following is correct?

A. The training loss increases while the validation loss decreases when training the model.
B. The training loss decreases while the validation loss increases when training the model.
C. The training loss stays constant and the validation loss decreases when training the model.
D. The training loss .stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

Explanation:
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
References:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

Question: 59

You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. an F1 score that is high
B. an R Squared value dose to 1
C. an R-Squared value close to 0
D. a Root Mean Square Error value that is high
E. a Root Mean Square Error value that is tow
F. an F 1 score that is low.

Question: 60

You are evaluating, a completed binary classification machine learning model. You need to use the precision as the evaluation metric.
Which visualization should you use?

A. Binary classification confusion matrix
B. box plot
D. coefficient of determination

Question: 61

You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B
The variation of Class B is very high. You need to resolve imbalances.
Which method should you use?

A. Partition and Sample
B. Cluster Centroids
D. Synthetic Minority Oversampling Technique (SMOTE)

Question: 62

HOTSPOT
You have a dataset contains 2,000 rows. You arc building a machine learning classification model by using Azure Machine Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
• Divide the data into subsets.
• Assign the rows into folds using a round-robin method.
• Allow rows in the dataset to be reused.
How should you configure the module? To answer select the appropriate Options m the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Question: 63

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:
• Learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
• Batch size must be 16, 32 and 64.
• 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.

Question: 64

You are a data scientist building a deep convolutional neural network (CNN) for image classification. The CNN model you built shows signs of overfitting.

You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Reduce the amount of training data.
B. Add an additional dense layer with 64 input units
C. Add L1/L2 regularization.
D. Use training data augmentation
E. Add an additional dense layer with 512 input units.

Question: 65

You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module. Which splitting mode should you use?

A. Regular Expression Split
B. Split Rows with the Randomized split parameter set to true
C. Relative Expression Split
D. Recommender Split

Question: 66

HOTSPOT
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains
10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? 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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