diff --git a/examples/getting-started-movielens/04-Triton-Inference-with-TF.ipynb b/examples/getting-started-movielens/04-Triton-Inference-with-TF.ipynb
index d4a3d881c..ab2cc2960 100644
--- a/examples/getting-started-movielens/04-Triton-Inference-with-TF.ipynb
+++ b/examples/getting-started-movielens/04-Triton-Inference-with-TF.ipynb
@@ -327,9 +327,10 @@
     }
    ],
    "source": [
-    "batch = df_lib.read_parquet(\n",
-    "    os.path.join(INPUT_DATA_DIR, \"valid.parquet\"), num_rows=3, columns=[\"userId\", \"movieId\"]\n",
+    "valid = df_lib.read_parquet(\n",
+    "    os.path.join(INPUT_DATA_DIR, \"valid.parquet\"), columns=[\"userId\", \"movieId\"]\n",
     ")\n",
+    "batch = valid[:3]\n",
     "print(batch)"
    ]
   },
diff --git a/examples/scaling-criteo/04-Triton-Inference-with-HugeCTR.ipynb b/examples/scaling-criteo/04-Triton-Inference-with-HugeCTR.ipynb
index 07841ebd7..0d875274e 100644
--- a/examples/scaling-criteo/04-Triton-Inference-with-HugeCTR.ipynb
+++ b/examples/scaling-criteo/04-Triton-Inference-with-HugeCTR.ipynb
@@ -516,11 +516,11 @@
     "df_lib = get_lib()\n",
     "input_cols = workflow.input_schema.column_names\n",
     "# read in data for request\n",
-    "batch = df_lib.read_parquet(\n",
+    "data = df_lib.read_parquet(\n",
     "    os.path.join(sorted(glob.glob(original_data_path + \"/*.parquet\"))[-1]),\n",
-    "    num_rows=3,\n",
     "    columns=input_cols\n",
     ")\n",
+    "batch = data[:3]\n",
     "batch = batch[[x for x in batch.columns if x not in ['label']]]\n",
     "batch"
    ]
diff --git a/examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb b/examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb
index a8a038829..ecc8ba1c2 100644
--- a/examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb
+++ b/examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb
@@ -270,11 +270,11 @@
     "df_lib = get_lib()\n",
     "input_cols = workflow.input_schema.column_names\n",
     "# read in data for request\n",
-    "batch = df_lib.read_parquet(\n",
+    "data = df_lib.read_parquet(\n",
     "    os.path.join(sorted(glob.glob(original_data_path + \"/*.parquet\"))[-1]),\n",
-    "    num_rows=3,\n",
     "    columns=input_cols\n",
     ")\n",
+    "batch = data[:3]\n",
     "batch"
    ]
   },
diff --git a/tests/unit/examples/test_building_deploying_multi_stage_RecSys.py b/tests/unit/examples/test_building_deploying_multi_stage_RecSys.py
index 225b8d756..ebb7a2b0b 100644
--- a/tests/unit/examples/test_building_deploying_multi_stage_RecSys.py
+++ b/tests/unit/examples/test_building_deploying_multi_stage_RecSys.py
@@ -65,11 +65,11 @@ def test_func():
             from merlin.dataloader.tf_utils import configure_tensorflow
             configure_tensorflow()
             df_lib = get_lib()
-            batch = df_lib.read_parquet(
+            train = df_lib.read_parquet(
                 os.path.join("/tmp/data/processed_nvt/", "train", "part_0.parquet"),
-                num_rows=1,
                 columns=["user_id_raw"],
             )
+            batch = train[:1]
             from merlin.systems.triton.utils import run_ensemble_on_tritonserver
             response = run_ensemble_on_tritonserver(
                 "/tmp/examples/poc_ensemble", ensemble.graph.input_schema, batch, outputs,  "executor_model"