To efficiently count records in SQL, one typically utilizes the `COUNT()` function, which is optimal for determining the number of rows that meet a specific condition within a dataset. A basic implementation can be achieved with the syntax `SELECT COUNT(*) FROM table_name;`, where `table_name` is replaced by the actual name of the table you are querying. If you need to apply a filter, incorporating a `WHERE` clause is essential, for example, `SELECT COUNT(*) FROM table_name WHERE condition;`. This function is generally used in reporting and data analysis to extract aggregate information about the dataset.

For more complex conditions, leveraging `GROUP BY` can provide counts on subsets of data. This can be particularly useful when analyzing records based on distinct values in a specific column. For instance, using `SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name;` allows a count of records for each distinct value in `column_name`. Additionally, when performance is critical, consider adding appropriate indexing on columns involved in filtering or grouping, as this can significantly speed up count operations, especially in large datasets. Fine-tuning your queries with such optimizations not only enhances efficiency but also provides deeper insights into the data.