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Free DP-100 Exam Braindumps (page: 21)

Page 20 of 127
PDF with 512 Questions

HOTSPOT (Drag and Drop is not supported)
You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. The text column represents a product's category. The product category will always be one of the following:
-Bikes
-Cars
-Vans
-Boats
You are building a regression model using the scikit-learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package.
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.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: pandas as df
Pandas takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example.
Box 2: transpose[ProductCategoryMapping]
Reshape the data from the pandas Series to columns.


Reference:

https://datascienceplus.com/linear-regression-in-python/



You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access.
Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students.
You need to ensure that students can run Python-based data visualization code.
Which Azure tool should you use?

  1. Anaconda Data Science Platform
  2. Azure BatchAI
  3. Azure Notebooks
  4. Azure Machine Learning Service

Answer(s): C

Explanation:


Reference:

https://notebooks.azure.com/



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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

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 ג€fully conditional specificationג€ or ג€sequential regression multiple imputationג€ 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



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: Remove the entire column that contains the missing data point.
Does the solution meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

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






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