diff --git a/python-package/xgboost/spark/core.py b/python-package/xgboost/spark/core.py
index 0e9509099e13..ca1acdeaebb6 100644
--- a/python-package/xgboost/spark/core.py
+++ b/python-package/xgboost/spark/core.py
@@ -114,10 +114,10 @@
 
 _unsupported_fit_params = {
     "sample_weight",  # Supported by spark param weightCol
-    # Supported by spark param weightCol # and validationIndicatorCol
-    "eval_set",
-    "sample_weight_eval_set",
+    "eval_set",  # Supported by spark param validation_indicator_col
+    "sample_weight_eval_set",  # Supported by spark param weight_col + validation_indicator_col
     "base_margin",  # Supported by spark param base_margin_col
+    "base_margin_eval_set",  # Supported by spark param base_margin_col + validation_indicator_col
     "group",  # Use spark param `qid_col` instead
     "qid",  # Use spark param `qid_col` instead
     "eval_group",  # Use spark param `qid_col` instead
@@ -287,6 +287,14 @@ def _validate_params(self):
                 "If features_cols param set, then features_col param is ignored."
             )
 
+        if self.getOrDefault(self.objective) is not None:
+            if not isinstance(self.getOrDefault(self.objective), str):
+                raise ValueError("Only string type 'objective' param is allowed.")
+
+        if self.getOrDefault(self.eval_metric) is not None:
+            if not isinstance(self.getOrDefault(self.eval_metric), str):
+                raise ValueError("Only string type 'eval_metric' param is allowed.")
+
         if self.getOrDefault(self.enable_sparse_data_optim):
             if self.getOrDefault(self.missing) != 0.0:
                 # If DMatrix is constructed from csr / csc matrix, then inactive elements
@@ -578,7 +586,6 @@ def _get_distributed_train_params(self, dataset):
         params.update(fit_params)
         params["verbose_eval"] = verbose_eval
         classification = self._xgb_cls() == XGBClassifier
-        num_classes = int(dataset.select(countDistinct(alias.label)).collect()[0][0])
         if classification:
             num_classes = int(
                 dataset.select(countDistinct(alias.label)).collect()[0][0]
@@ -610,6 +617,10 @@ def _get_xgb_train_call_args(cls, train_params):
                 kwargs_params[key] = value
             else:
                 booster_params[key] = value
+
+        booster_params = {
+            k: v for k, v in booster_params.items() if k not in _non_booster_params
+        }
         return booster_params, kwargs_params
 
     def _fit(self, dataset):
diff --git a/python-package/xgboost/spark/estimator.py b/python-package/xgboost/spark/estimator.py
index dbf080944cce..7336b597167a 100644
--- a/python-package/xgboost/spark/estimator.py
+++ b/python-package/xgboost/spark/estimator.py
@@ -211,6 +211,11 @@ class SparkXGBClassifier(_SparkXGBEstimator, HasProbabilityCol, HasRawPrediction
 
     def __init__(self, **kwargs):
         super().__init__()
+        # The default 'objective' param value comes from sklearn `XGBClassifier` ctor,
+        # but in pyspark we will automatically set objective param depending on
+        # binary or multinomial input dataset, and we need to remove the fixed default
+        # param value as well to avoid causing ambiguity.
+        self._setDefault(objective=None)
         self.setParams(**kwargs)
 
     @classmethod
@@ -227,6 +232,10 @@ def _validate_params(self):
             raise ValueError(
                 "Spark Xgboost classifier estimator does not support `qid_col` param."
             )
+        if self.getOrDefault(self.objective):  # pylint: disable=no-member
+            raise ValueError(
+                "Setting custom 'objective' param is not allowed in 'SparkXGBClassifier'."
+            )
 
 
 _set_pyspark_xgb_cls_param_attrs(SparkXGBClassifier, SparkXGBClassifierModel)
diff --git a/tests/python/test_spark/test_spark_local.py b/tests/python/test_spark/test_spark_local.py
index 14377663c388..58c313ea043f 100644
--- a/tests/python/test_spark/test_spark_local.py
+++ b/tests/python/test_spark/test_spark_local.py
@@ -433,6 +433,7 @@ def test_regressor_params_basic(self):
         self.assertEqual(py_reg.n_estimators.parent, py_reg.uid)
         self.assertFalse(hasattr(py_reg, "gpu_id"))
         self.assertEqual(py_reg.getOrDefault(py_reg.n_estimators), 100)
+        self.assertEqual(py_reg.getOrDefault(py_reg.objective), "reg:squarederror")
         py_reg2 = SparkXGBRegressor(n_estimators=200)
         self.assertEqual(py_reg2.getOrDefault(py_reg2.n_estimators), 200)
         py_reg3 = py_reg2.copy({py_reg2.max_depth: 10})
@@ -445,6 +446,7 @@ def test_classifier_params_basic(self):
         self.assertEqual(py_cls.n_estimators.parent, py_cls.uid)
         self.assertFalse(hasattr(py_cls, "gpu_id"))
         self.assertEqual(py_cls.getOrDefault(py_cls.n_estimators), 100)
+        self.assertEqual(py_cls.getOrDefault(py_cls.objective), None)
         py_cls2 = SparkXGBClassifier(n_estimators=200)
         self.assertEqual(py_cls2.getOrDefault(py_cls2.n_estimators), 200)
         py_cls3 = py_cls2.copy({py_cls2.max_depth: 10})