diff --git a/sagemaker-inference-recommender/huggingface-inference-recommender/huggingface-inference-recommender.ipynb b/sagemaker-inference-recommender/huggingface-inference-recommender/huggingface-inference-recommender.ipynb
index 99cc314e2e..ef3ca53449 100644
--- a/sagemaker-inference-recommender/huggingface-inference-recommender/huggingface-inference-recommender.ipynb
+++ b/sagemaker-inference-recommender/huggingface-inference-recommender/huggingface-inference-recommender.ipynb
@@ -694,7 +694,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)"
    ]
   },
diff --git a/sagemaker-inference-recommender/inference-recommender.ipynb b/sagemaker-inference-recommender/inference-recommender.ipynb
index 427094b56a..f493ed319c 100644
--- a/sagemaker-inference-recommender/inference-recommender.ipynb
+++ b/sagemaker-inference-recommender/inference-recommender.ipynb
@@ -757,7 +757,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]
@@ -876,7 +877,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]
diff --git a/sagemaker-inference-recommender/sklearn-inference-recommender/sklearn-inference-recommender.ipynb b/sagemaker-inference-recommender/sklearn-inference-recommender/sklearn-inference-recommender.ipynb
index 5b682e8ed7..bda343d8ca 100644
--- a/sagemaker-inference-recommender/sklearn-inference-recommender/sklearn-inference-recommender.ipynb
+++ b/sagemaker-inference-recommender/sklearn-inference-recommender/sklearn-inference-recommender.ipynb
@@ -638,7 +638,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)"
    ]
   },
@@ -788,7 +789,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]
diff --git a/sagemaker-inference-recommender/tensorflow-cloudwatch/tf-cloudwatch-inference-recommender.ipynb b/sagemaker-inference-recommender/tensorflow-cloudwatch/tf-cloudwatch-inference-recommender.ipynb
index 689a4717ec..ec97f091a0 100644
--- a/sagemaker-inference-recommender/tensorflow-cloudwatch/tf-cloudwatch-inference-recommender.ipynb
+++ b/sagemaker-inference-recommender/tensorflow-cloudwatch/tf-cloudwatch-inference-recommender.ipynb
@@ -631,7 +631,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]
@@ -826,7 +827,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]
diff --git a/sagemaker-inference-recommender/xgboost/xgboost-inference-recommender.ipynb b/sagemaker-inference-recommender/xgboost/xgboost-inference-recommender.ipynb
index 3ae7e16924..a0dfe49986 100644
--- a/sagemaker-inference-recommender/xgboost/xgboost-inference-recommender.ipynb
+++ b/sagemaker-inference-recommender/xgboost/xgboost-inference-recommender.ipynb
@@ -509,6 +509,54 @@
     "print(\"ModelPackage Version ARN : {}\".format(model_package_arn))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative Option: ContainerConfig\n",
+    "\n",
+    "If you are missing mandatory fields to create an inference recommender job in your model package version like so (this `create_model_package_input_dict` is missing `Domain`, `Task`, and `SamplePayloadUrl`):\n",
+    "\n",
+    "```\n",
+    "create_model_package_input_dict = {\n",
+    "    \"ModelPackageGroupName\": model_package_group_name,\n",
+    "    \"ModelPackageDescription\": model_package_description,\n",
+    "    \"ModelApprovalStatus\": model_approval_status,\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "You may define the fields `Domain`, `Task`, and `SamplePayloadUrl` in the optional field `ContainerConfig` like so:\n",
+    "\n",
+    "```\n",
+    "payload_config = {\n",
+    "    \"SamplePayloadUrl\": sample_payload_url,\n",
+    "}\n",
+    "\n",
+    "container_config = {\n",
+    "    \"Domain\": ml_domain.upper(),\n",
+    "    \"Task\": ml_task.upper(),\n",
+    "    \"PayloadConfig\": payload_config,\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "And then provide it directly within `create_inference_recommendations_job()` API like so:\n",
+    "\n",
+    "```\n",
+    "default_response = client.create_inference_recommendations_job(\n",
+    "    JobName=str(default_job),\n",
+    "    JobDescription=\"\",\n",
+    "    JobType=\"Default\",\n",
+    "    RoleArn=role,\n",
+    "    InputConfig={\n",
+    "        \"ModelPackageVersionArn\": model_package_arn,\n",
+    "        \"ContainerConfig\": container_config\n",
+    "    },\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "For more information on what else can be provided via `ContainerConfig` please refer to the `CreateInferenceRecommendationsJob` doc here: [CreateInferenceRecommendationsJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -606,7 +654,8 @@
     "    for x in inference_recommender_job[\"InferenceRecommendations\"]\n",
     "]\n",
     "df = pd.DataFrame(data)\n",
-    "df.drop(\"VariantName\", inplace=True, axis=1)\n",
+    "dropFilter = df.filter([\"VariantName\"])\n",
+    "df.drop(dropFilter, inplace=True, axis=1)\n",
     "pd.set_option(\"max_colwidth\", 400)\n",
     "df.head()"
    ]