diff --git a/cli/batch-score.sh b/cli/batch-score.sh
index 8d7e36c961..ff25f9119f 100644
--- a/cli/batch-score.sh
+++ b/cli/batch-score.sh
@@ -39,7 +39,7 @@ sleep 60
 
 echo "Invoking batch endpoint with public URI (MNIST)"
 # <start_batch_scoring_job>
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
 # </start_batch_scoring_job>
 
 echo "Showing job detail"
@@ -71,13 +71,13 @@ fi
 echo "Invoke batch endpoint with specific output file name"
 # <start_batch_scoring_job_configure_output_settings>
 export OUTPUT_FILE_NAME=predictions_`echo $RANDOM`.csv
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --output-path azureml://datastores/workspaceblobstore/paths/$ENDPOINT_NAME --set output_file_name=$OUTPUT_FILE_NAME --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2.blob.core.windows.net/data/mnist/sample --input-type uri_folder --output-path azureml://datastores/workspaceblobstore/paths/$ENDPOINT_NAME --set output_file_name=$OUTPUT_FILE_NAME --query name -o tsv)
 # </start_batch_scoring_job_configure_output_settings>
 
 echo "Invoke batch endpoint with specific overwrites"
 # <start_batch_scoring_job_overwrite>
 export OUTPUT_FILE_NAME=predictions_`echo $RANDOM`.csv
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --mini-batch-size 20 --instance-count 5 --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2.blob.core.windows.net/data/mnist/sample --input-type uri_folder --mini-batch-size 20 --instance-count 5 --query name -o tsv)
 # </start_batch_scoring_job_overwrite>
 
 echo "Stream job detail"
@@ -114,7 +114,7 @@ az ml batch-deployment create --file endpoints/batch/deploy-models/mnist-classif
 echo "Invoke batch endpoint with public data"
 # <test_new_deployment>
 DEPLOYMENT_NAME="mnist-keras-dpl"
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --deployment-name $DEPLOYMENT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --deployment-name $DEPLOYMENT_NAME --input https://azuremldata2.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
 # </test_new_deployment>
 
 echo "Show job detail"
@@ -155,7 +155,7 @@ az ml batch-endpoint show --name $ENDPOINT_NAME --query "{Name:name, Defaults:de
 
 echo "Invoke batch endpoint with the new default deployment with public URI"
 # <test_new_default_deployment>
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
 # </test_new_default_deployment>
 
 echo "Stream job logs to console"
@@ -198,7 +198,7 @@ RESPONSE=$(curl --location --request POST "$SCORING_URI" \
   \"properties\": {
     \"dataset\": {
       \"dataInputType\": \"DataUrl\",
-      \"Path\": \"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample\"
+      \"Path\": \"https://azuremldata2.blob.core.windows.net/data/mnist/sample\"
     }
   }
 }")
diff --git a/sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb b/sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb
index 60b47d7c4d..99cb1e3c26 100644
--- a/sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb
+++ b/sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb
@@ -29,7 +29,8 @@
     "* `lightgbm==1.5.2`\n",
     "* `numpy`\n",
     "* `pandas`\n",
-    "* `pyarrow`"
+    "* `pyarrow`\n",
+    "* `azure`"
    ]
   },
   {
@@ -477,7 +478,7 @@
     "environment = Environment(\n",
     "    name=\"batch-mlflow-xgboost\",\n",
     "    conda_file=\"environment/conda.yaml\",\n",
-    "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
+    "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n",
     ")"
    ]
   },
@@ -636,7 +637,7 @@
    "source": [
     "input = Input(\n",
     "    type=AssetTypes.URI_FOLDER,\n",
-    "    path=\"https://azuremlexampledata.blob.core.windows.net/data/heart-disease-uci/data\",\n",
+    "    path=\"https://azuremldata2.blob.core.windows.net/data/heart-disease-uci/data\",\n",
     ")"
    ]
   },
diff --git a/sdk/python/endpoints/batch/deploy-models/mnist-classifier/mnist-batch.ipynb b/sdk/python/endpoints/batch/deploy-models/mnist-classifier/mnist-batch.ipynb
index 85bd5d680f..24d3ced823 100644
--- a/sdk/python/endpoints/batch/deploy-models/mnist-classifier/mnist-batch.ipynb
+++ b/sdk/python/endpoints/batch/deploy-models/mnist-classifier/mnist-batch.ipynb
@@ -406,7 +406,7 @@
     "env = Environment(\n",
     "    name=\"batch-torch-py38\",\n",
     "    conda_file=\"deployment-torch/environment/conda.yaml\",\n",
-    "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
+    "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n",
     ")"
    ]
   },
@@ -576,7 +576,7 @@
     "    endpoint_name=endpoint_name,\n",
     "    deployment_name=deployment.name,\n",
     "    input=Input(\n",
-    "        path=\"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample/\",\n",
+    "        path=\"https://azuremldata2.blob.core.windows.net/data/mnist/sample/\",\n",
     "        type=AssetTypes.URI_FOLDER,\n",
     "    ),\n",
     ")"
@@ -770,7 +770,7 @@
     "job = ml_client.batch_endpoints.invoke(\n",
     "    endpoint_name=endpoint_name,\n",
     "    input=Input(\n",
-    "        path=\"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample/\",\n",
+    "        path=\"https://azuremldata2.blob.core.windows.net/data/mnist/sample/\",\n",
     "        type=AssetTypes.URI_FOLDER,\n",
     "    ),\n",
     "    params_override=[\n",
@@ -801,9 +801,7 @@
    "source": [
     "job = ml_client.batch_endpoints.invoke(\n",
     "    endpoint_name=endpoint_name,\n",
-    "    input=Input(\n",
-    "        path=\"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample/\"\n",
-    "    ),\n",
+    "    input=Input(path=\"https://azuremldata2.blob.core.windows.net/data/mnist/sample/\"),\n",
     "    params_override=[{\"mini_batch_size\": \"20\"}, {\"compute.instance_count\": \"5\"}],\n",
     ")"
    ]
@@ -1082,7 +1080,7 @@
     "    endpoint_name=endpoint_name,\n",
     "    deployment_name=deployment_keras.name,\n",
     "    input=Input(\n",
-    "        path=\"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample/\",\n",
+    "        path=\"https://azuremldata2.blob.core.windows.net/data/mnist/sample/\",\n",
     "        type=AssetTypes.URI_FOLDER,\n",
     "    ),\n",
     ")"