diff --git a/dataproc/snippets/submit_spark_job_to_driver_node_group_cluster.py b/dataproc/snippets/submit_spark_job_to_driver_node_group_cluster.py
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+#!/usr/bin/env python
+
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This sample walks a user through submitting a Spark job to a
+# Dataproc driver node group cluster using the Dataproc
+# client library.
+
+# Usage:
+#    python submit_spark_job_to_driver_node_group_cluster.py \
+#        --project_id <PROJECT_ID> --region <REGION> \
+#        --cluster_name <CLUSTER_NAME>
+
+# [START dataproc_submit_spark_job_to_driver_node_group_cluster]
+
+import re
+
+from google.cloud import dataproc_v1 as dataproc
+from google.cloud import storage
+
+
+def submit_job(project_id: str, region: str, cluster_name: str) -> None:
+    """Submits a Spark job to the specified Dataproc cluster with a driver node group and prints the output.
+
+    Args:
+        project_id: The Google Cloud project ID.
+        region: The Dataproc region where the cluster is located.
+        cluster_name: The name of the Dataproc cluster.
+    """
+    # Create the job client.
+    with dataproc.JobControllerClient(
+        client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
+    ) as job_client:
+
+        driver_scheduling_config = dataproc.DriverSchedulingConfig(
+            memory_mb=2048,  # Example memory in MB
+            vcores=2,  # Example number of vcores
+        )
+
+        # Create the job config. 'main_jar_file_uri' can also be a
+        # Google Cloud Storage URL.
+        job = {
+            "placement": {"cluster_name": cluster_name},
+            "spark_job": {
+                "main_class": "org.apache.spark.examples.SparkPi",
+                "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
+                "args": ["1000"],
+            },
+            "driver_scheduling_config": driver_scheduling_config
+        }
+
+        operation = job_client.submit_job_as_operation(
+            request={"project_id": project_id, "region": region, "job": job}
+        )
+
+        response = operation.result()
+
+        # Dataproc job output gets saved to the Cloud Storage bucket
+        # allocated to the job. Use a regex to obtain the bucket and blob info.
+        matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
+        if not matches:
+            print(f"Error: Could not parse driver output URI: {response.driver_output_resource_uri}")
+            raise ValueError
+
+        output = (
+            storage.Client()
+            .get_bucket(matches.group(1))
+            .blob(f"{matches.group(2)}.000000000")
+            .download_as_bytes()
+            .decode("utf-8")
+        )
+
+        print(f"Job finished successfully: {output}")
+
+# [END dataproc_submit_spark_job_to_driver_node_group_cluster]
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Submits a Spark job to a Dataproc driver node group cluster."
+    )
+    parser.add_argument("--project_id", help="The Google Cloud project ID.", required=True)
+    parser.add_argument("--region", help="The Dataproc region where the cluster is located.", required=True)
+    parser.add_argument("--cluster_name", help="The name of the Dataproc cluster.", required=True)
+
+    args = parser.parse_args()
+    submit_job(args.project_id, args.region, args.cluster_name)