Which of the following is a proper use case for the APPROX_COUNT_DISTINCT function?

The APPROX_COUNT_DISTINCT function is designed to provide an estimate of the number of unique values in large datasets, which makes it a suitable choice for scenarios where an exact count might be computationally expensive or resource-intensive. This function utilizes algorithms that allow for approximate calculations, which can significantly enhance performance, especially in big data scenarios, while still yielding results that are statistically reliable.

In contrast, the exact counting of rows is better achieved using standard COUNT functions, which do not provide estimates and can be resource-heavy on large datasets. Summarizing data with GROUP BY typically requires precise counts or values, making it incompatible with the use of approximation methods. Inserting data into a table is unrelated, as this function is focused on querying and counting data rather than performing data manipulation tasks.

Therefore, using APPROX_COUNT_DISTINCT is most appropriate when needing to quickly estimate unique values in large datasets without the demand for exact figures.