A model trained on historical customer service data is found to be biased against customers from a specific region, primarily because that region is underrepresented in the training data. The team has collected more data from that region, but it is still not enough to balance the dataset. Which of the following mitigation strategies would be most effective to address the underrepresentation?

Use a different encoding scheme for the region variable.

Remove the region variable from the model entirely.

Apply a global normalization technique to all features.

Oversample the underrepresented region using a technique like SMOTE.

Why is it important to document cleaning steps in a data auditing process?

To automatically correct errors

To ensure reproducibility and transparency of the data cleaning process

To increase the speed of data processing

To reduce the storage size of the dataset

What does 'imputation' refer to in the context of handling null values?

Flagging missing values for later review

Replacing missing values with substituted estimates

Removing all rows that contain any missing value

Converting missing values to a special numeric code

Which of the following is an example of parsing data?

Splitting a full name column into first and last name columns

Replacing missing values with the mean

Standardizing date formats to YYYY-MM-DD

CertNexusCertified Data Science Practitioner (CDSP)

Domain 2Objective 2

Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

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 11–15

11foundation · easy
Which summary statistic is most sensitive to outliers?
Select an answer first
12expert · hard
A model trained on historical customer service data is found to be biased against customers from a specific region, primarily because that region is underrepresented in the training data. The team has collected more data from that region, but it is still not enough to balance the dataset. Which of the following mitigation strategies would be most effective to address the underrepresentation?
Select an answer first
13foundation · easy
Why is it important to document cleaning steps in a data auditing process?
Select an answer first
14foundation · easy
What does 'imputation' refer to in the context of handling null values?
Select an answer first
15foundation · easy
Which of the following is an example of parsing data?
Select an answer first

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Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

Questions 11–15

Which summary statistic is most sensitive to outliers?

Why is it important to document cleaning steps in a data auditing process?

What does 'imputation' refer to in the context of handling null values?

Which of the following is an example of parsing data?

Keep studying — 2.0 Extracting, Transforming, and Loading Data