diff --git a/ingestion/src/metadata/data_quality/validations/base_test_handler.py b/ingestion/src/metadata/data_quality/validations/base_test_handler.py
index 98e3a28ae6d..984bbd40623 100644
--- a/ingestion/src/metadata/data_quality/validations/base_test_handler.py
+++ b/ingestion/src/metadata/data_quality/validations/base_test_handler.py
@@ -347,13 +347,14 @@ class BaseTestValidator(ABC):
         if failed_rows is None:
             failed_rows = total_rows - passed_rows
 
-        # Calculate percentages with rounding to 2 decimal places
-        passed_rows_percentage = (
-            round(passed_rows / total_rows * 100, 2) if total_rows > 0 else 0
-        )
-        failed_rows_percentage = (
-            round(failed_rows / total_rows * 100, 2) if total_rows > 0 else 0
-        )
+        # Calculate percentages - derive one from the other to ensure they sum to 100%
+        if total_rows > 0:
+            passed_rows_percentage = round(passed_rows / total_rows * 100, 2)
+            # Derive failed percentage to ensure sum equals 100%
+            failed_rows_percentage = round(100 - passed_rows_percentage, 2)
+        else:
+            passed_rows_percentage = 0
+            failed_rows_percentage = 0
 
         # Convert dictionary to array of DimensionValue objects
         dimension_values_array = [
diff --git a/ingestion/src/metadata/data_quality/validations/column/base/columnValuesToBeUnique.py b/ingestion/src/metadata/data_quality/validations/column/base/columnValuesToBeUnique.py
index cefb1289691..53d7d55e64f 100644
--- a/ingestion/src/metadata/data_quality/validations/column/base/columnValuesToBeUnique.py
+++ b/ingestion/src/metadata/data_quality/validations/column/base/columnValuesToBeUnique.py
@@ -51,7 +51,7 @@ class BaseColumnValuesToBeUniqueValidator(BaseTestValidator):
         try:
             column: Union[SQALikeColumn, Column] = self._get_column_name()
             count = self._run_results(Metrics.COUNT, column)
-            unique_count = self._get_unique_count(Metrics.UNIQUE_COUNT, column)
+            unique_count = self._get_unique_count(Metrics.DISTINCT_COUNT, column)
         except (ValueError, RuntimeError) as exc:
             msg = f"Error computing {self.test_case.fullyQualifiedName}: {exc}"  # type: ignore
             logger.debug(traceback.format_exc())
diff --git a/ingestion/src/metadata/data_quality/validations/column/pandas/columnValuesToBeInSet.py b/ingestion/src/metadata/data_quality/validations/column/pandas/columnValuesToBeInSet.py
index 364e54269e2..8b2d0d6d84d 100644
--- a/ingestion/src/metadata/data_quality/validations/column/pandas/columnValuesToBeInSet.py
+++ b/ingestion/src/metadata/data_quality/validations/column/pandas/columnValuesToBeInSet.py
@@ -143,19 +143,19 @@ class ColumnValuesToBeInSetValidator(
                         {
                             "dimension": dimension_value,
                             "count_in_set": count_in_set,
-                            "row_count": row_count,
                             DIMENSION_TOTAL_COUNT_KEY: row_count,
                             DIMENSION_FAILED_COUNT_KEY: failed_count,
                         }
                     )
                 else:
-                    # Non-enum mode
+                    # Non-enum mode: we only care about matches, not failures
+                    # Following SQLAlchemy's logic exactly
                     results_data.append(
                         {
                             "dimension": dimension_value,
                             "count_in_set": count_in_set,
-                            DIMENSION_TOTAL_COUNT_KEY: count_in_set,
-                            DIMENSION_FAILED_COUNT_KEY: 0,
+                            DIMENSION_TOTAL_COUNT_KEY: count_in_set,  # Use count_in_set as total
+                            DIMENSION_FAILED_COUNT_KEY: 0,  # Don't track failures in non-enum mode
                         }
                     )
 
@@ -184,14 +184,17 @@ class ColumnValuesToBeInSetValidator(
                     # Extract metric values
                     count_in_set = int(row.get("count_in_set", 0))
 
+                    # Follow SQLAlchemy's exact logic
                     if match_enum:
-                        total_count = int(row.get("row_count", 0))
+                        # Enum mode: track actual totals and failures
+                        total_count = int(row.get(DIMENSION_TOTAL_COUNT_KEY, 0))
                         failed_count = int(row.get(DIMENSION_FAILED_COUNT_KEY, 0))
-                        matched = total_count - count_in_set == 0
+                        matched = failed_count == 0  # All values must be in enum
                     else:
-                        total_count = count_in_set
-                        failed_count = 0
-                        matched = count_in_set > 0
+                        # Non-enum mode: we only care about matches
+                        matched = count_in_set > 0  # Pass if ANY values are in set
+                        total_count = count_in_set  # Use count_in_set as total
+                        failed_count = 0  # Don't track failures
 
                     impact_score = float(row.get("impact_score", 0.0))