diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb
index 02902c19af..fcd91207cc 100644
--- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb
+++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb
@@ -244,7 +244,7 @@
     "|**target_column_name**|The name of the label column.|\n",
     "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
     "|**training_data**|The training data to be used for this experiment. You can use a registered MLTable in the workspace using the format `<mltable_name>:<version>` OR you can use a local file or folder as a MLTable. For e.g `Input(mltable='my_mltable:1')` OR `Input(mltable=MLTable(local_path=\"./data\"))` The parameter 'training_data' must always be provided.|\n",
-    "|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it with any other compute in the workspace.|\n",
+    "|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'energy-cluster-v2' present in the workspace. You can replace it with any other compute in the workspace.|\n",
     "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
     "|**name**|The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
     "|**experiment_name**|The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment. For example, if a user runs this notebook multiple times, there will be multiple runs associated with the same Experiment name.|\n",
diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb
index 6b7814e7c3..021ec57a0a 100644
--- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb
+++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb
@@ -172,7 +172,7 @@
     "- `training_data` - The data to be used for training. It should contain both training feature columns and a target column. Optionally, this data can be split for segregating a validation or test dataset. \n",
     "You can use a registered MLTable in the workspace using the format '<mltable_name>:<version>' OR you can use a local file or folder as a MLTable. For e.g Input(mltable='my_mltable:1') OR Input(mltable=MLTable(local_path=\"./data\"))\n",
     "The parameter 'training_data' must always be provided.\n",
-    "- `compute` - The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it any other compute in the workspace. \n",
+    "- `compute` - The compute on which the AutoML job will run. In this example we are using a compute called 'adv-energy-cluster-v2' present in the workspace. You can replace it any other compute in the workspace. \n",
     "- `name` - The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
     "- `experiment_name` - The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment.\n",
     "\n",
@@ -228,7 +228,7 @@
    "outputs": [],
    "source": [
     "# general job parameters\n",
-    "compute_name = \"cpu-cluster\"\n",
+    "compute_name = \"adv-energy-cluster-v2\"\n",
     "max_trials = 5\n",
     "exp_name = \"dpv2-forecasting-experiment\""
    ]
diff --git a/sdk/python/notebooks_config.ini b/sdk/python/notebooks_config.ini
index e22e4be9fb..7e2c191ecd 100644
--- a/sdk/python/notebooks_config.ini
+++ b/sdk/python/notebooks_config.ini
@@ -16,7 +16,7 @@ COMPUTE_NAMES = bike-share-v2
 
 [automl-forecasting-task-energy-demand-advanced]
 USE_FORECAST_REQUIREMENTS = 0
-COMPUTE_NAMES = cpu-cluster
+COMPUTE_NAMES = adv-energy-cluster-v2
 
 [automl-forecasting-task-energy-demand-advanced-mlflow]
 USE_FORECAST_REQUIREMENTS = 1