ExamGecko
Question list
Search
Search

List of questions

Search

Related questions











Question 75 - DP-100 discussion

Report
Export

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
Answers
A.
Computer Linear Correlation
B.
Export Count Table
Answers
B.
Export Count Table
C.
Execute Python Script
Answers
C.
Execute Python Script
D.
Convert to Indicator Values
Answers
D.
Convert to Indicator Values
E.
Summarize Data
Answers
E.
Summarize Data
Suggested answer: B, 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

asked 02/10/2024
Lucia Montero Tejeda
37 questions
User
Your answer:
0 comments
Sorted by

Leave a comment first