Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 271:

  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 count for each feature column.

  Which two modules can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Computer Linear Correlation

  B. Export Count Table

  C. Execute Python Script

  D. Convert to Indicator Values

  E. Summarize Data

  Correct Answer: BE

  The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer (deprecated) modules.

  E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:

  How many missing values are there in each column?

  How many unique values are there in a feature column?

  What is the mean and standard deviation for each column? The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.

  Incorrect Answers:

  A: The Compute Linear Correlation module in Azure Machine Learning Studio is used to compute a set of Pearson correlation coefficients for each possible pair of variables in the input dataset.

  C: With Python, you can perform tasks that aren't currently supported by existing Studio modules such as:

  Visualizing data using matplotlib

  Using Python libraries to enumerate datasets and models in your workspace Reading, loading, and manipulating data from sources not supported by the Import Data module

  D: The purpose of the Convert to Indicator Values module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data

• Question 272:

  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?

  A. Yes

  B. No

  Correct Answer: B

  Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

  References:

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

• Question 273:

  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 Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

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

• Question 274:

  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 (MDL) binning mode.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  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 quantized.

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

• Question 275:

  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 normalization with a QuantileIndex normalization.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  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 276:

  You are solving a classification task.

  You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.

  You need to configure the k parameter for the cross-validation.

  Which value should you use?

  A. k=0.5

  B. k=10

  C. k=5

  D. k=1

  Correct Answer: B

  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.

• Question 277:

  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?

  A. Assign Data to Clusters

  B. Load Trained Model

  C. Partition and Sample

  D. Tune Model-Hyperparameters

  Correct Answer: C

  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 278:

  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?

  A. Replace with mean

  B. Remove entire column

  C. Remove entire row

  D. Hot Deck

  E. Custom substitution value

  F. Replace with mode

  Correct Answer: C

  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 279:

  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?

  A. Yes

  B. No

  Correct Answer: B

  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 280:

  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 analyzing a numerical dataset which contains missing values in several columns.

  You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

  You need to analyze a full dataset to include all values.

  Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Use the Multiple Imputation by Chained Equations (MICE) method.

  References:

  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

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