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Question No.1

You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.

You need to select a data cleaning method. Which method should you use?

  1. Synthetic Minority Oversampling Technique (SMOTE)

  2. Replace using MICE

  3. Replace using; Probabilistic PCA

  4. Normalization

Correct Answer: A

Question No.2

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 using Azure Machine learning Studio to perform feature engineering on a dataset. You need to normalize values to produce a feature column grouped into bins.

Solution: Apply an Entropy Minimum Description Length (MDI) binning mode. Does the solution meet the goal?

  1. Yes

  2. No

Correct Answer: A

Explanation:

Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named

lt;colnamegt;quantized.

References:

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

Question No.3

HOTSPOT

You have a feature set containing the following numerical features: X, Y, and Z.

The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:

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.

image

Correct Answer:

image

Question No.4

HOTSPOT

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

You must use a Receiver Operating Characteristic (ROC) 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.

image

Correct Answer:

image

Question No.5

You use Azure Machine Learning Studio to build a machine learning experiment. You need to divide data into two distinct datasets.

Which module should you use?

  1. Partition and Sample

  2. Assign Data to Clusters

  3. Group Data into Bins

  4. Test Hypothesis Using t-Test

Correct Answer: A

Explanation:

Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and- sample

Question No.6

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.

image

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.

image

Correct Answer:

image

Question No.7

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 creating a new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than tin- other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Principal Components Analysis (PCA) sampling mode.

Does the solution meet the goal?

  1. Yes

  2. No

Correct Answer: B

Question No.8

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 creating a model to predict the price of a student#39;s artwork depending on the following variables: the student#39;s length of education, degree type, and art form.

You start by creating a linear regression model. You need to evaluate the linear regression model.

Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.

Does the solution meet the goal?

  1. Yes

  2. No

Correct Answer: B

Explanation:

Relative Squared Error, Coefficient of Determination are good metrics to evaluate the linear regression model, but the others are metrics for classification models.

References:

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

Question No.9

HOTSPOT

You have a Python data frame named salesData in the following format: The data frame must be unpivoted to a long data format as follows:

You need to use the pandas.melt() function in Python to perform the transformation.

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.

image

Correct Answer:

image

Question No.10

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 creating a new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Scale and Reduce sampling mode.

Does the solution meet the goal?

  1. Yes

  2. No

Correct Answer: B

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