What does the AUC score represent in model evaluation?

The accuracy of the model on the training set

The number of true positives in the test set

The probability that the model ranks a random positive instance higher than a random negative instance

The precision of the model at a fixed threshold

When is it most appropriate to remove records with null values rather than impute them?

When the proportion of missing data is very small and the records are not critical

When the dataset is small and every record is valuable

When the missing values are in the target variable for supervised learning

When the missing values are in a categorical variable

CertNexusCertified Data Science Practitioner (CDSP)

Domain 2Objective 2

Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 5)

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 21–25

21foundation · easy
What does the AUC score represent in model evaluation?
Select an answer first
22foundation · easy
Which metric is used to evaluate the trade-off between true positive rate and false positive rate?
Select an answer first
23foundation · easy
A dataset has 95% of its records with all fields filled, but the remaining 5% have missing values. Which data quality dimension is being assessed?
Select an answer first
24application · medium
A data analyst is assessing the quality of a customer address dataset. They find that 5% of the records have a null 'postal_code', and 2% have a 'state' value that does not match the 'postal_code'. The data was last updated 6 months ago. Which dimension of data quality is most clearly compromised by the mismatched 'state' and 'postal_code' values?
Select an answer first
25foundation · easy
When is it most appropriate to remove records with null values rather than impute them?
Select an answer first

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Objective 2.2 Clean Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 5)

Questions 21–25

What does the AUC score represent in model evaluation?

Which metric is used to evaluate the trade-off between true positive rate and false positive rate?

A dataset has 95% of its records with all fields filled, but the remaining 5% have missing values. Which data quality dimension is being assessed?

When is it most appropriate to remove records with null values rather than impute them?

Keep studying — 2.0 Extracting, Transforming, and Loading Data