Microsoft DP-100 Practice Test - Questions Answers, Page 8

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

<colname>quantized.

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

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

Reference:

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

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.

Incorrect Answers:

Common data tasks for the Scale and Reduce sampling mode include clipping, binning, and normalizing numerical values.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/data-transformation-scale-and-reduce

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.

Reference:

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

Instead use the Multiple Imputation by Chained Equations (MICE) method.

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study.

Reference:

https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

Reference: https://notebooks.azure.com/

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.

Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/ https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

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

Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/ https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.

Reference:

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