diff --git a/samples/notebooks/Lightweight Python components - basics.ipynb b/samples/notebooks/Lightweight Python components - basics.ipynb
index 7328ae0b9ce..a36f13dd2e4 100644
--- a/samples/notebooks/Lightweight Python components - basics.ipynb	
+++ b/samples/notebooks/Lightweight Python components - basics.ipynb	
@@ -103,7 +103,7 @@
     "#Advanced function\n",
     "#Demonstrates imports, helper functions and multiple outputs\n",
     "from typing import NamedTuple\n",
-    "def my_divmod(dividend: float, divisor:float, output_dir:str = './') -> NamedTuple('MyDivmodOutput', [('quotient', float), ('remainder', float)]):\n",
+    "def my_divmod(dividend: float, divisor:float) -> NamedTuple('MyDivmodOutput', [('quotient', float), ('remainder', float)]):\n",
     "    '''Divides two numbers and calculate  the quotient and remainder'''\n",
     "    #Imports inside a component function:\n",
     "    import numpy as np\n",
@@ -114,8 +114,12 @@
     "\n",
     "    (quotient, remainder) = divmod_helper(dividend, divisor)\n",
     "\n",
-    "    from tensorflow.python.lib.io import file_io\n",
     "    import json\n",
+    "    from pathlib import Path\n",
+    "\n",
+    "    #FIX: Metadata and metrics should be output like all other outputs\n",
+    "    ui_metadata_output_path='/mlpipeline-ui-metadata.json'\n",
+    "    metrics_output_path='/mlpipeline-metrics.json'\n",
     "    \n",
     "    # Exports a sample tensorboard:\n",
     "    metadata = {\n",
@@ -124,8 +128,10 @@
     "        'source': 'gs://ml-pipeline-dataset/tensorboard-train',\n",
     "      }]\n",
     "    }\n",
-    "    with open(output_dir + 'mlpipeline-ui-metadata.json', 'w') as f:\n",
-    "      json.dump(metadata, f)\n",
+    "    metadata_text = json.dumps(metadata)\n",
+    "    metadata_path = Path(ui_metadata_output_path)\n",
+    "    metadata_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    metadata_path.write_text(metadata_text) #FIX: Remove explicit writing once artifact handling is fixed in the compiler\n",
     "\n",
     "    # Exports two sample metrics:\n",
     "    metrics = {\n",
@@ -136,9 +142,10 @@
     "          'name': 'remainder',\n",
     "          'numberValue':  float(remainder),\n",
     "        }]}\n",
-    "\n",
-    "    with file_io.FileIO(output_dir + 'mlpipeline-metrics.json', 'w') as f:\n",
-    "        json.dump(metrics, f)\n",
+    "    metrics_text = json.dumps(metadata)\n",
+    "    metrics_path = Path(metrics_output_path)\n",
+    "    metrics_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    metrics_path.write_text(metrics_text)\n",
     "\n",
     "    from collections import namedtuple\n",
     "    divmod_output = namedtuple('MyDivmodOutput', ['quotient', 'remainder'])\n",
@@ -208,7 +215,7 @@
     "    \n",
     "    #Passing a task output reference as operation arguments\n",
     "    #For an operation with a single return value, the output reference can be accessed using `task.output` or `task.outputs['output_name']` syntax\n",
-    "    divmod_task = divmod_op(add_task.output, b, '/')\n",
+    "    divmod_task = divmod_op(add_task.output, b)\n",
     "\n",
     "    #For an operation with a multiple return values, the output references can be accessed using `task.outputs['output_name']` syntax\n",
     "    result_task = add_op(divmod_task.outputs['quotient'], c)"