DA0-002 Data Type Consistency Practice Question
Network Topology
A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?
⚠ Common exam trap
The trap here is that candidates often focus on statistical concerns like outliers or missing values, but the most pressing issue is data type inconsistency, which prevents the model from executing at all. This question tests the order of operations in data preprocessing: fix structural issues first.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Inconsistent data in total_charges
Inconsistent data formats, such as mixed numeric representations in a column like 'total_charges', will cause parsing errors when loading data into a logistic regression model. Inconsistent data must be standardized (e.g., to a uniform numeric type) before any other preprocessing steps, as it prevents the model from executing. This issue is more critical to address first than missing values or outliers, which can be handled later through imputation or transformation without blocking model execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Duplicate customer IDs
Why it's wrong here
All IDs appear unique.
- ✗
Missing values in total_charges
Why it's wrong here
No missing values are present.
- ✓
Inconsistent data in total_charges
Why this is correct
The total_charges for the first customer is equal to monthly_charges, suggesting a calculation error.
- ✗
Outliers in monthly_charges
Why it's wrong here
Values like 99.65 are not extreme outliers.
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