diff --git a/basic_and_faq_usages/simple_artifact_store.py b/basic_and_faq_usages/simple_artifact_store.py
index 85af235d..5d9bc13d 100644
--- a/basic_and_faq_usages/simple_artifact_store.py
+++ b/basic_and_faq_usages/simple_artifact_store.py
@@ -1,4 +1,5 @@
 import os
+import tempfile
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -36,7 +37,7 @@ def create_dataset(dataset_path):
 dataset = create_dataset(dataset_path)
 
 
-def plot_predictions(a, b, trial):
+def plot_predictions(a, b, trial, tmp_dir):
     # Create an artifact, which is figure in this example, to upload.
     _, ax = plt.subplots()
     x = np.linspace(-5, 5, 100)
@@ -45,17 +46,20 @@ def plot_predictions(a, b, trial):
     ax.set_title(f"a={a:.2f}, b={b:.2f}")
     ax.grid()
     ax.legend()
-    plt.savefig(fig_name)
+    plt.savefig(os.path.join(tmp_dir, fig_name))
     plt.close()
 
 
 def objective(trial):
     a = trial.suggest_float("a", -5, 5)
     b = trial.suggest_float("b", -5, 5)
-    plot_predictions(a, b, trial)
 
-    # Link the plotted figure with trial using artifact store API.
-    upload_artifact(artifact_store=artifact_store, file_path=fig_name, study_or_trial=trial)
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        print(tmp_dir)
+        plot_predictions(a, b, trial, tmp_dir)
+        fig_path = os.path.join(tmp_dir, fig_name)
+        # Link the plotted figure with trial using artifact store API.
+        upload_artifact(artifact_store=artifact_store, file_path=fig_path, study_or_trial=trial)
 
     return np.mean((a * dataset["X"]**2 + b - dataset["Y"]) ** 2)