diff --git a/notebooks/examples/python/Quickstart/QuickstartNotebook.ipynb b/notebooks/examples/python/Quickstart/QuickstartNotebook.ipynb
index 6ca738f02..9fab5af77 100644
--- a/notebooks/examples/python/Quickstart/QuickstartNotebook.ipynb
+++ b/notebooks/examples/python/Quickstart/QuickstartNotebook.ipynb
@@ -12,6 +12,7 @@
     }
    },
    "source": [
+    "%md\n",
     "# Mosaic Quickstart\n",
     "\n",
     "> Perform a point-in-polygon spatial join between NYC Taxi trips and zones. __Note: this does not get into performance tweaks that are available for scaled joins.__\n",
@@ -29,13 +30,10 @@
     "  * Already installed with Mosaic, use `%%mosaic_kepler` magic [[Mosaic Docs](https://databrickslabs.github.io/mosaic/usage/kepler.html)]\n",
     "  * Import with `from keplergl import KeplerGl` to use directly\n",
     "\n",
-    "If you have trouble with Volume access:\n",
-    "\n",
-    "* For Mosaic 0.3 series (< DBR 13)     - you can copy resources to DBFS as a workaround\n",
-    "* For Mosaic 0.4 series (DBR 13.3 LTS) - you will need to either copy resources to DBFS or setup for Unity Catalog + Shared Access which will involve your workspace admin. Instructions, as updated, will be [here](https://databrickslabs.github.io/mosaic/usage/install-gdal.html).\n",
+    "If you have trouble access source datasets from a Unity Catalog Volume, the easiest workaround is to copy resources to an accessible location DBFS. Note: 'Shared' access mode clusters are not supported at all.\n",
     "\n",
     "--- \n",
-    " __Last Update__ 28 NOV 2023 [Mosaic 0.3.12]"
+    " __Last Update__ 10 JUN 2024 [Mosaic 0.4.2]"
    ]
   },
   {
@@ -81,8 +79,7 @@
     }
    ],
    "source": [
-    "%pip install \"databricks-mosaic<0.4,>=0.3\" --quiet # <- Mosaic 0.3 series\n",
-    "# %pip install \"databricks-mosaic<0.5,>=0.4\" --quiet # <- Mosaic 0.4 series (as available)"
+    "%pip install \"databricks-mosaic<0.5,>=0.4\" --quiet # <- Mosaic 0.4 series"
    ]
   },
   {
@@ -109,8 +106,7 @@
     "spark.conf.set(\"spark.sql.shuffle.partitions\", 1_024)                  # <-- default is 200\n",
     "\n",
     "# -- import databricks + spark functions\n",
-    "from pyspark.sql import functions as F\n",
-    "from pyspark.sql.functions import col, udf\n",
+    "from pyspark.sql.functions import col, udf, lit, to_json, explode, array\n",
     "from pyspark.sql.types import *\n",
     "\n",
     "# -- setup mosaic\n",
@@ -398,8 +394,8 @@
     "    .option(\"multiline\", \"true\")\n",
     "    .format(\"json\")\n",
     "    .load(zone_dir)\n",
-    "    .select(\"type\", explode(col(\"features\")).alias(\"feature\"))\n",
-    "    .select(\"type\", col(\"feature.properties\").alias(\"properties\"), to_json(col(\"feature.geometry\")).alias(\"json_geometry\"))\n",
+    "    .select(\"type\", F.explode(col(\"features\")).alias(\"feature\"))\n",
+    "    .select(\"type\", col(\"feature.properties\").alias(\"properties\"), F.to_json(col(\"feature.geometry\")).alias(\"json_geometry\"))\n",
     "    .withColumn(\"geometry\", mos.st_aswkt(mos.st_geomfromgeojson(\"json_geometry\")))\n",
     ")\n",
     "\n",
@@ -1857,7 +1853,7 @@
     }
    },
    "source": [
-    "> We can easily perform a similar join for the drop off location. __Note: in this case using `withPickupZone` from above as the left sid of the join.__"
+    "> We can easily perform a similar join for the drop off location. __Note: in this case using `withPickupZone` from above as the left side of the join.__"
    ]
   },
   {