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Free DP-100 Exam Braindumps (page: 20)

Page 19 of 127
PDF with 512 Questions

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?

  1. Yes
  2. No

Answer(s): A

Explanation:

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



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

Answer(s): B

Explanation:

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



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.

  1. Computer Linear Correlation
  2. Export Count Table
  3. Execute Python Script
  4. Convert to Indicator Values
  5. Summarize Data

Answer(s): C,E

Explanation:

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



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: Use the Last Observation Carried Forward (LOCF) method to impute the missing data points.
Does the solution meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

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/






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