To identify non-unique data in a column based on Count and DistinctCount, which key indicator is relevant?

When identifying non-unique data within a column, the key indicator of interest is the relationship between the total count of entries and the distinct count of unique values. A high count alongside a low distinct count suggests that many duplicate values are present.

In this scenario, a high count means that there are many instances of records in that column, while a low distinct count indicates that only a few unique values are represented in those records. This situation typically points to redundancy, meaning that the same values are repeated multiple times, highlighting the non-unique nature of the data.

The other indicators are less relevant for determining non-uniqueness. Equal count and distinct count imply that every entry is unique, which contradicts the presence of non-unique data. A low overall count does not necessarily indicate non-uniqueness, as it could suggest that there are a few unique values, but without the high repetition characteristic. Lastly, zero distinct values would indicate an absence of data rather than non-uniqueness, since there would be no repeated values to consider.

Thus, the correct choice is supported by the evidence of high duplicative entries alongside few distinct values in the dataset.