How can 'sampling' be used as a bias mitigation technique?

By converting categorical data to numeric

By oversampling the minority class to balance the dataset

By sorting the data by a key column

By randomly deleting rows from the dataset

What is the purpose of implementing validation rules in data cleaning?

To detect corrupted data by checking values against expected patterns

To increase the speed of data processing

To automatically correct all errors without human review

To combine multiple data sources into one

A retail analytics team receives a daily sales export where the 'revenue' column sometimes contains values like '12,345.67' (with comma thousands separators) and other times '12345.67'. The 'date' column mixes 'MM/DD/YYYY' and 'YYYY-MM-DD' formats. Before loading into a database for aggregation, what is the most appropriate cleaning step?

Standardize the revenue column to a single numeric type and the date column to a single ISO format.

Remove all rows where the revenue value contains a comma to avoid parsing errors.

Load the data as-is and rely on the database to handle the mixed formats during queries.

Convert the revenue column to text and the date column to text to preserve the original values.

What is 'data provenance'?

The origin and history of the data

The storage location of the data

The process of removing duplicates

Which action is an example of correcting a corrupt value?

Changing the data type of a column from string to integer

Deleting all rows with any missing value

Replacing a negative age with the median age of the dataset

Merging two rows that have the same name

CertNexusCertified Data Science Practitioner (CDSP)

Domain 2Objective 2

Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Part of the 2.0 Extracting, Transforming, and Loading Data domain, which accounts for 17-25% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

51questions here

11free pages

18concepts

17-25%of the exam

18 Concepts in This Objective

Identify and eliminate irregularities in data
Handle nulls
Remove duplicates
Correct corrupt values
Parse data
Check for corrupted data
Correct data format
Deduplicate data
Apply risk and bias mitigation techniques
Understand common forms of ML bias
Identify sources of bias
Use exploratory data analysis for outlier detection
Assess data quality
Use data auditing techniques
Mitigate bias impact
Evaluate bias outcomes
Monitor and improve data cleaning process
Adhere to data governance rules

Questions 16–20

16foundation · easy
How can 'sampling' be used as a bias mitigation technique?
Select an answer first
17foundation · easy
What is the purpose of implementing validation rules in data cleaning?
Select an answer first
18application · medium
A retail analytics team receives a daily sales export where the 'revenue' column sometimes contains values like '12,345.67' (with comma thousands separators) and other times '12345.67'. The 'date' column mixes 'MM/DD/YYYY' and 'YYYY-MM-DD' formats. Before loading into a database for aggregation, what is the most appropriate cleaning step?
Select an answer first
19foundation · easy
What is 'data provenance'?
Select an answer first
20foundation · easy
Which action is an example of correcting a corrupt value?
Select an answer first

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