Question 10
UnclassifiedA data wrangling step almost always required before modeling is:
Correct answer: B
Explanation
Handling missing values, outliers, and inconsistent data types is a standard preprocessing step because models require clean, numeric, and internally consistent inputs. Data wrangling prepares raw data for analysis by fixing errors and irregularities before modeling can produce reliable results.
Why each option is right or wrong
A. Hyperparameter tuning
B. Handling missing values, outliers, and inconsistent data types
Pre-model preparation ordinarily includes cleaning the dataset so the inputs are usable by the algorithm: missing entries must be imputed or removed, extreme values assessed, and fields converted to consistent types. In practice, most modeling libraries will fail or produce unreliable estimates if variables are left with nulls, mixed text/numeric formats, or unaddressed anomalies, so this step is a prerequisite before fitting any model.
C. Saving the model artifact
D. Choosing the loss function