D-DS-FN-23 Exam Dumps : Dell Data Science Foundations

K-means clustering is best suited for numeric data, as it relies on calculating the Euclidean distance between data points, which is meaningful for numerical values.

When building a K-means clustering model, it's important to ensure that the data is properly scaled, as K-means is sensitive to the scale of the variables. If the variables have different scales, the clustering may not segment as expected. Standardizing or normalizing the data can often improve the results.

Recalibration of the model should be assessed during the Operationalize phase, as this is when the model is deployed and monitored in real-world conditions, making it easier to identify if it requires adjustments based on its performance.