Which transformation should be selected to convert columns into attribute-value pairs in a Fabric data flow?

The correct transformation to convert columns into attribute-value pairs in a Fabric data flow is to use the option that involves unpivoting other columns. This transformation specifically focuses on taking selected columns in a dataset that hold values and turning them into a more normalized format, where each value from the selected columns becomes part of a new row in combination with the other attributes, presenting them as pairs.

When transforming data, particularly in scenarios requiring a shift from a wide format (like having multiple columns representing various attributes) to a narrow format (where you have one column for attribute names and one column for their corresponding values), selecting the right unpivot transformation is essential. By specifying which columns to unpivot, it allows for flexible handling of diverse and complex datasets, making the data structure easier to analyze and visualize in subsequent processing steps.

Other options, while relevant in some contexts, do not directly address the task of converting columns into attribute-value pairs as effectively. For instance, grouping data does not achieve the same outcome, instead aggregating values without maintaining a one-to-one mapping of attributes and their respective values. Removing other columns simply reduces the dataset without facilitating the desired transformation into pairs.