diff --git a/metadata-ingestion/src/datahub/ingestion/source/ge_data_profiler.py b/metadata-ingestion/src/datahub/ingestion/source/ge_data_profiler.py
index 9f6ac9dd21164..4998a96d8e5f0 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/ge_data_profiler.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/ge_data_profiler.py
@@ -629,7 +629,16 @@ def generate_dataset_profile(  # noqa: C901 (complexity)
         self.query_combiner.flush()
 
         assert profile.rowCount is not None
-        row_count: int = profile.rowCount
+        row_count: int  # used for null counts calculation
+        if profile.partitionSpec and "SAMPLE" in profile.partitionSpec.partition:
+            # We can alternatively use `self._get_dataset_rows(profile)` to get
+            # exact count of rows in sample, as actual rows involved in sample
+            # may be slightly different (more or less) than configured `sample_size`.
+            # However not doing so to start with, as that adds another query overhead
+            # plus approximate metrics should work for sampling based profiling.
+            row_count = self.config.sample_size
+        else:
+            row_count = profile.rowCount
 
         for column_spec in columns_profiling_queue:
             column = column_spec.column
@@ -781,7 +790,7 @@ def update_dataset_batch_use_sampling(self, profile: DatasetProfileClass) -> Non
             sample_pc = 100 * self.config.sample_size / profile.rowCount
             sql = (
                 f"SELECT * FROM {str(self.dataset._table)} "
-                + f"TABLESAMPLE SYSTEM ({sample_pc:.3f} percent)"
+                + f"TABLESAMPLE SYSTEM ({sample_pc:.8f} percent)"
             )
             temp_table_name = create_bigquery_temp_table(
                 self,