diff --git a/mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py b/mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py
index 8c73037261..6112328e1b 100644
--- a/mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py
+++ b/mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py
@@ -20,7 +20,6 @@
 import ConfigSpace
 import numpy.typing as npt
 import pandas as pd
-from smac.utils.configspace import convert_configurations_to_array
 
 from mlos_core.data_classes import Observation, Observations, Suggestion
 from mlos_core.optimizers.bayesian_optimizers.bayesian_optimizer import (
@@ -121,6 +120,10 @@ def __init__(
         from smac.main.config_selector import ConfigSelector
         from smac.random_design.probability_design import ProbabilityRandomDesign
         from smac.runhistory import TrialInfo
+        from smac.utils.configspace import convert_configurations_to_array
+
+        # Save util function here as a property for later usage, also to satisfy linter
+        self._convert_configurations_to_array = convert_configurations_to_array
 
         # Store for TrialInfo instances returned by .ask()
         self.trial_info_map: Dict[ConfigSpace.Configuration, TrialInfo] = {}
@@ -411,7 +414,7 @@ def surrogate_predict(self, suggestion: Suggestion) -> npt.NDArray:
         if self.base_optimizer._config_selector._model is None:
             raise RuntimeError("Surrogate model is not yet trained")
 
-        config_array = convert_configurations_to_array(
+        config_array = self._convert_configurations_to_array(
             [
                 ConfigSpace.Configuration(
                     self.optimizer_parameter_space, values=suggestion.config.to_dict()