diff --git a/dataproc/README.md b/dataproc/README.md
index 16beb58eec3..8fe04973304 100644
--- a/dataproc/README.md
+++ b/dataproc/README.md
@@ -8,7 +8,7 @@ demonstrated here could also be accomplished using the Cloud Console or the gclo
 `list_clusters.py` is a simple command-line program to demonstrate connecting to the
 Dataproc API and listing the clusters in a ergion
 
-`create_cluster_and_submit_jbo.py` demonstrates how to create a cluster, submit the 
+`create_cluster_and_submit_job.py` demonstrates how to create a cluster, submit the 
 `pyspark_sort.py` job, download the output from Google Cloud Storage, and output the result.
 
 ## Prerequisites to run locally:
diff --git a/dataproc/create_cluster_and_submit_job.py b/dataproc/create_cluster_and_submit_job.py
index 9077f926c74..0cf58ad74c5 100644
--- a/dataproc/create_cluster_and_submit_job.py
+++ b/dataproc/create_cluster_and_submit_job.py
@@ -166,7 +166,6 @@ def wait_for_job(dataproc, project, job_id):
             jobId=job_id).execute()
         # Handle exceptions
         if result['status']['state'] == 'ERROR':
-            print(result)
             raise Exception(result['status']['details'])
         elif result['status']['state'] == 'DONE':
             print('Job finished')