diff --git a/tests/tune/noniterative/test_study.py b/tests/tune/noniterative/test_study.py
index 1a47dd9..f8f5716 100644
--- a/tests/tune/noniterative/test_study.py
+++ b/tests/tune/noniterative/test_study.py
@@ -3,9 +3,11 @@
 import pandas as pd
 from fugue import FugueWorkflow
 from pytest import raises
+
 from tune import optimize_noniterative, suggest_for_noniterative_objective
 from tune.concepts.dataset import TuneDatasetBuilder
 from tune.concepts.flow import Monitor
+from tune.concepts.logger import MetricLogger, get_current_metric_logger
 from tune.concepts.space import Grid, Space
 from tune.constants import TUNE_REPORT, TUNE_REPORT_METRIC
 from tune.exceptions import TuneInterrupted
@@ -14,23 +16,29 @@
 
 
 def objective(a: float, b: pd.DataFrame) -> float:
-    return a ** 2 + b.shape[0]
+    return a**2 + b.shape[0]
+
+
+def objective_with_logger(a: float, b: pd.DataFrame) -> float:
+    m = get_current_metric_logger()
+    assert m.mock
+    return a**2 + b.shape[0]
 
 
 def objective2(a: float, b: pd.DataFrame) -> float:
-    return -(a ** 2 + b.shape[0])
+    return -(a**2 + b.shape[0])
 
 
 def objective3(a: float, b: pd.DataFrame) -> float:
     if a == -2:
         raise TuneInterrupted()
-    return a ** 2 + b.shape[0]
+    return a**2 + b.shape[0]
 
 
 def objective4(a: float, b: pd.DataFrame) -> float:
     if a == -2:
         raise ValueError("expected")
-    return a ** 2 + b.shape[0]
+    return a**2 + b.shape[0]
 
 
 def assert_metric(df: pd.DataFrame, metrics: List[float]) -> None:
@@ -56,12 +64,15 @@ def test_study(tmpdir):
     # no data partition
     builder = TuneDatasetBuilder(space, str(tmpdir)).add_df("b", dag.df(input_df))
     dataset = builder.build(dag, 1)
+    logger = MetricLogger()
+    logger.mock = True
     for distributed in [True, False, None]:
         # min_better = True
         result = optimize_noniterative(
-            objective=objective,
+            objective=objective_with_logger,
             dataset=dataset,
             distributed=distributed,
+            logger=logger,
         )
         result.result()[[TUNE_REPORT, TUNE_REPORT_METRIC]].output(
             assert_metric, params=dict(metrics=[3.0, 4.0, 7.0])