CompTIA Data+ DA0-002 (V2) Practice Question

The correct answer is Scaling. Scaling, which includes techniques like normalization (Min-Max scaling) and standardization (Z-score scaling), is used to transform the values of numeric features to a similar scale. This is crucial for distance-based algorithms, such as k-means clustering, where features with larger ranges can dominate the distance calculations and skew the results. By scaling annual_income and customer_satisfaction_score to a common range (e.g., 0 to 1), the analyst ensures both features contribute more equitably to the clustering model.

• Binning is incorrect because it involves grouping a range of continuous values into a smaller number of discrete 'bins' or categories. This is used to simplify data or convert it to a categorical format, not to address the influence of different numeric scales in a distance-based algorithm.
• Imputation is the process of replacing missing values in a dataset. The scenario does not mention any missing data, making this technique irrelevant to the problem described.
• Parsing is the process of converting unstructured or semi-structured data (like text strings or log files) into a structured format. This is not applicable to transforming the scale of existing numerical features.