A data team is building a new analytics platform. They need to ensure that every transformation applied to the source data can be traced back to its origin and that the cleaning steps are well-documented. Which practice is most important to implement?

Automate the cleaning process with a scheduling tool.

Create a backup of the raw data before any cleaning is performed.

Use a single, centralized database for all cleaned data.

Maintain a detailed data lineage and audit log for all cleaning steps.

A logistics company receives shipment tracking data as a single text field in the format 'Origin: NYC, Destination: LAX, Weight: 12.5kg, Date: 2023-10-26'. An analyst needs to analyze average shipping weight by route. What is the most critical cleaning step to enable this analysis?

Check for any null values in the text field.

Remove the 'kg' suffix from the weight values.

Standardize the date format to 'YYYY-MM-DD'.

Parse the text field into separate columns for origin, destination, weight, and date.

Which of the following is an example of correcting data format?

Replacing outliers with the median

Converting all date strings to a single format like YYYY-MM-DD

Removing rows with missing values

Which of the following is an example of a bias mitigation technique applied during data cleaning?

Sorting the data by a key column

Converting all categorical variables to numeric codes

Oversampling a minority class to balance the dataset

Removing all outliers to simplify the data

CertNexusCertified Data Science Practitioner (CDSP)

Domain 2Objective 2

Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 10)

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 46–50

46application · medium
A data team is building a new analytics platform. They need to ensure that every transformation applied to the source data can be traced back to its origin and that the cleaning steps are well-documented. Which practice is most important to implement?
Select an answer first
47application · medium
A logistics company receives shipment tracking data as a single text field in the format 'Origin: NYC, Destination: LAX, Weight: 12.5kg, Date: 2023-10-26'. An analyst needs to analyze average shipping weight by route. What is the most critical cleaning step to enable this analysis?
Select an answer first
48foundation · easy
Which of the following is an example of correcting data format?
Select an answer first
49foundation · easy
Which of the following is an example of a bias mitigation technique applied during data cleaning?
Select an answer first
50foundation · easy
Which dimension of data quality refers to whether the data is up-to-date?
Select an answer first

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