diff --git a/samples/tables/notebooks/energy_price_forecasting/energy_price_forecasting.ipynb b/samples/tables/notebooks/energy_price_forecasting/energy_price_forecasting.ipynb
index e1f429bb..681ad410 100644
--- a/samples/tables/notebooks/energy_price_forecasting/energy_price_forecasting.ipynb
+++ b/samples/tables/notebooks/energy_price_forecasting/energy_price_forecasting.ipynb
@@ -343,7 +343,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "You can import your data to AutoML Tables from GCS or BigQuery. For this tutorial, you can use the [iris dataset](https://storage.cloud.google.com/rostam-193618-tutorial/automl-tables-v1beta1/iris.csv) as your training data. You can create a GCS bucket and upload the  data into your bucket. The URI for your file is `gs://BUCKET_NAME/FOLDER_NAME1/FOLDER_NAME2/.../FILE_NAME`. Alternatively you can create a BigQuery table and upload the data into the table. The URI for your table is `bq://PROJECT_ID.DATASET_ID.TABLE_ID`.\n",
+        "You can import your data to AutoML Tables from GCS or BigQuery. You can create a GCS bucket and upload the  data into your bucket. The URI for your file is `gs://BUCKET_NAME/FOLDER_NAME1/FOLDER_NAME2/.../FILE_NAME`. Alternatively you can create a BigQuery table and upload the data into the table. The URI for your table is `bq://PROJECT_ID.DATASET_ID.TABLE_ID`.\n",
         "\n",
         "Importing data may take a few minutes or hours depending on the size of your data. If your Colab times out, run the following command to retrieve your dataset. Replace `dataset_name` with its actual value obtained in the preceding cells.\n",
         "\n",