diff --git a/tests/test_distributed/test_with_spark/test_spark_local.py b/tests/test_distributed/test_with_spark/test_spark_local.py
index 01f3bd12e626..3664f06e4706 100644
--- a/tests/test_distributed/test_with_spark/test_spark_local.py
+++ b/tests/test_distributed/test_with_spark/test_spark_local.py
@@ -1327,32 +1327,37 @@ def test_unsupported_params(self):
             SparkXGBClassifier(evals_result={})
 
 
-class XgboostRankerLocalTest(SparkTestCase):
-    def setUp(self):
-        self.session.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", "8")
-        self.ranker_df_train = self.session.createDataFrame(
-            [
-                (Vectors.dense(1.0, 2.0, 3.0), 0, 0),
-                (Vectors.dense(4.0, 5.0, 6.0), 1, 0),
-                (Vectors.dense(9.0, 4.0, 8.0), 2, 0),
-                (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 0, 1),
-                (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, 1),
-                (Vectors.sparse(3, {1: 8.0, 2: 9.5}), 2, 1),
-            ],
-            ["features", "label", "qid"],
-        )
-        self.ranker_df_test = self.session.createDataFrame(
-            [
-                (Vectors.dense(1.5, 2.0, 3.0), 0, -1.87988),
-                (Vectors.dense(4.5, 5.0, 6.0), 0, 0.29556),
-                (Vectors.dense(9.0, 4.5, 8.0), 0, 2.36570),
-                (Vectors.sparse(3, {1: 1.0, 2: 6.0}), 1, -1.87988),
-                (Vectors.sparse(3, {1: 6.0, 2: 7.0}), 1, -0.30612),
-                (Vectors.sparse(3, {1: 8.0, 2: 10.5}), 1, 2.44826),
-            ],
-            ["features", "qid", "expected_prediction"],
-        )
-        self.ranker_df_train_1 = self.session.createDataFrame(
+LTRData = namedtuple(
+    "LTRData", ("df_train", "df_test", "df_train_1")
+)
+
+
+@pytest.fixture
+def ltr_data(spark: SparkSession) -> Generator[LTRData, None, None]:
+    spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", "8")
+    ranker_df_train = spark.createDataFrame(
+        [
+            (Vectors.dense(1.0, 2.0, 3.0), 0, 0),
+            (Vectors.dense(4.0, 5.0, 6.0), 1, 0),
+            (Vectors.dense(9.0, 4.0, 8.0), 2, 0),
+            (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 0, 1),
+            (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, 1),
+            (Vectors.sparse(3, {1: 8.0, 2: 9.5}), 2, 1),
+        ],
+        ["features", "label", "qid"],
+    )
+    ranker_df_test = spark.createDataFrame(
+        [
+            (Vectors.dense(1.5, 2.0, 3.0), 0, -1.75218),
+            (Vectors.dense(4.5, 5.0, 6.0), 0, -0.34192949533462524),
+            (Vectors.dense(9.0, 4.5, 8.0), 0, 1.7251298427581787),
+            (Vectors.sparse(3, {1: 1.0, 2: 6.0}), 1, -1.7521828413009644),
+            (Vectors.sparse(3, {1: 6.0, 2: 7.0}), 1, -1.0988065004348755),
+            (Vectors.sparse(3, {1: 8.0, 2: 10.5}), 1, 1.632674217224121),
+        ],
+        ["features", "qid", "expected_prediction"],
+    )
+    ranker_df_train_1 = spark.createDataFrame(
             [
                 (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 0, 9),
                 (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, 9),
@@ -1369,19 +1374,21 @@ def setUp(self):
             ]
             * 4,
             ["features", "label", "qid"],
-        )
+    )
+    yield LTRData(ranker_df_train, ranker_df_test, ranker_df_train_1)
 
-    def test_ranker(self):
+
+class TestPySparkLocalLETOR:
+    def test_ranker(self, ltr_data: LTRData) -> None:
         ranker = SparkXGBRanker(qid_col="qid", objective="rank:pairwise")
         assert ranker.getOrDefault(ranker.objective) == "rank:pairwise"
-        model = ranker.fit(self.ranker_df_train)
-        pred_result = model.transform(self.ranker_df_test).collect()
-
+        model = ranker.fit(ltr_data.df_train)
+        pred_result = model.transform(ltr_data.df_test).collect()
         for row in pred_result:
             assert np.isclose(row.prediction, row.expected_prediction, rtol=1e-3)
 
-    def test_ranker_qid_sorted(self):
+    def test_ranker_qid_sorted(self, ltr_data: LTRData) -> None:
         ranker = SparkXGBRanker(qid_col="qid", num_workers=4)
         assert ranker.getOrDefault(ranker.objective) == "rank:ndcg"
-        model = ranker.fit(self.ranker_df_train_1)
-        model.transform(self.ranker_df_test).collect()
+        model = ranker.fit(ltr_data.df_train_1)
+        model.transform(ltr_data.df_test).collect()