diff --git a/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_spark.ipynb b/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_spark.ipynb
index 0ec153dda9..1279b4b0d2 100644
--- a/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_spark.ipynb
+++ b/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_spark.ipynb
@@ -44,7 +44,7 @@
     "1. Explaining the importance of the various input features on the model's decision\n",
     "1. Accessing the reports through SageMaker Studio if you have an instance set up.\n",
     "\n",
-    "In doing so, the notebook will first train a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSONLines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
+    "In doing so, the notebook will first train a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
    ]
   },
   {
@@ -663,8 +663,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# For the sake of time, open a subset of the thousands of files\n",
+    "num_files_to_open = 100 # len(s3_files)\n",
     "local_shap_values = pd.DataFrame()\n",
-    "for file in s3_files:\n",
+    "for file in s3_files[num_files_to_open:]:\n",
     "    output = sagemaker.s3.S3Downloader.read_file(file)\n",
     "    df = pd.read_csv(StringIO(output), sep=\",\")\n",
     "    local_shap_values = local_shap_values.append(df, ignore_index=True)"