diff --git a/src/sagemaker/clarify.py b/src/sagemaker/clarify.py
index 6590d30514..3bc2071330 100644
--- a/src/sagemaker/clarify.py
+++ b/src/sagemaker/clarify.py
@@ -25,6 +25,8 @@
 
 import tempfile
 from abc import ABC, abstractmethod
+from typing import List, Union
+
 from sagemaker import image_uris, s3, utils
 from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor
 
@@ -922,6 +924,7 @@ def __init__(
             version (str): Clarify version to use.
         """  # noqa E501  # pylint: disable=c0301
         container_uri = image_uris.retrieve("clarify", sagemaker_session.boto_region_name, version)
+        self._last_analysis_config = None
         self.job_name_prefix = job_name_prefix
         super(SageMakerClarifyProcessor, self).__init__(
             role,
@@ -983,10 +986,10 @@ def _run(
                   the Trial Component will be unassociated.
                 * ``'TrialComponentDisplayName'`` is used for display in Amazon SageMaker Studio.
         """
-        analysis_config["methods"]["report"] = {
-            "name": "report",
-            "title": "Analysis Report",
-        }
+        # for debugging: to access locally, i.e. without a need to look for it in an S3 bucket
+        self._last_analysis_config = analysis_config
+        logger.info("Analysis Config: %s", analysis_config)
+
         with tempfile.TemporaryDirectory() as tmpdirname:
             analysis_config_file = os.path.join(tmpdirname, "analysis_config.json")
             with open(analysis_config_file, "w") as f:
@@ -1083,14 +1086,13 @@ def run_pre_training_bias(
                   the Trial Component will be unassociated.
                 * ``'TrialComponentDisplayName'`` is used for display in Amazon SageMaker Studio.
         """  # noqa E501  # pylint: disable=c0301
-        analysis_config = data_config.get_config()
-        analysis_config.update(data_bias_config.get_config())
-        analysis_config["methods"] = {"pre_training_bias": {"methods": methods}}
-        if job_name is None:
-            if self.job_name_prefix:
-                job_name = utils.name_from_base(self.job_name_prefix)
-            else:
-                job_name = utils.name_from_base("Clarify-Pretraining-Bias")
+        analysis_config = _AnalysisConfigGenerator.bias_pre_training(
+            data_config, data_bias_config, methods
+        )
+        # when name is either not provided (is None) or an empty string ("")
+        job_name = job_name or utils.name_from_base(
+            self.job_name_prefix or "Clarify-Pretraining-Bias"
+        )
         return self._run(
             data_config,
             analysis_config,
@@ -1165,21 +1167,13 @@ def run_post_training_bias(
                   the Trial Component will be unassociated.
                 * ``'TrialComponentDisplayName'`` is used for display in Amazon SageMaker Studio.
         """  # noqa E501  # pylint: disable=c0301
-        analysis_config = data_config.get_config()
-        analysis_config.update(data_bias_config.get_config())
-        (
-            probability_threshold,
-            predictor_config,
-        ) = model_predicted_label_config.get_predictor_config()
-        predictor_config.update(model_config.get_predictor_config())
-        analysis_config["methods"] = {"post_training_bias": {"methods": methods}}
-        analysis_config["predictor"] = predictor_config
-        _set(probability_threshold, "probability_threshold", analysis_config)
-        if job_name is None:
-            if self.job_name_prefix:
-                job_name = utils.name_from_base(self.job_name_prefix)
-            else:
-                job_name = utils.name_from_base("Clarify-Posttraining-Bias")
+        analysis_config = _AnalysisConfigGenerator.bias_post_training(
+            data_config, data_bias_config, model_predicted_label_config, methods, model_config
+        )
+        # when name is either not provided (is None) or an empty string ("")
+        job_name = job_name or utils.name_from_base(
+            self.job_name_prefix or "Clarify-Posttraining-Bias"
+        )
         return self._run(
             data_config,
             analysis_config,
@@ -1264,28 +1258,16 @@ def run_bias(
                   the Trial Component will be unassociated.
                 * ``'TrialComponentDisplayName'`` is used for display in Amazon SageMaker Studio.
         """  # noqa E501  # pylint: disable=c0301
-        analysis_config = data_config.get_config()
-        analysis_config.update(bias_config.get_config())
-        analysis_config["predictor"] = model_config.get_predictor_config()
-        if model_predicted_label_config:
-            (
-                probability_threshold,
-                predictor_config,
-            ) = model_predicted_label_config.get_predictor_config()
-            if predictor_config:
-                analysis_config["predictor"].update(predictor_config)
-            if probability_threshold is not None:
-                analysis_config["probability_threshold"] = probability_threshold
-
-        analysis_config["methods"] = {
-            "pre_training_bias": {"methods": pre_training_methods},
-            "post_training_bias": {"methods": post_training_methods},
-        }
-        if job_name is None:
-            if self.job_name_prefix:
-                job_name = utils.name_from_base(self.job_name_prefix)
-            else:
-                job_name = utils.name_from_base("Clarify-Bias")
+        analysis_config = _AnalysisConfigGenerator.bias(
+            data_config,
+            bias_config,
+            model_config,
+            model_predicted_label_config,
+            pre_training_methods,
+            post_training_methods,
+        )
+        # when name is either not provided (is None) or an empty string ("")
+        job_name = job_name or utils.name_from_base(self.job_name_prefix or "Clarify-Bias")
         return self._run(
             data_config,
             analysis_config,
@@ -1370,6 +1352,36 @@ def run_explainability(
                   the Trial Component will be unassociated.
                 * ``'TrialComponentDisplayName'`` is used for display in Amazon SageMaker Studio.
         """  # noqa E501  # pylint: disable=c0301
+        analysis_config = _AnalysisConfigGenerator.explainability(
+            data_config, model_config, model_scores, explainability_config
+        )
+        # when name is either not provided (is None) or an empty string ("")
+        job_name = job_name or utils.name_from_base(
+            self.job_name_prefix or "Clarify-Explainability"
+        )
+        return self._run(
+            data_config,
+            analysis_config,
+            wait,
+            logs,
+            job_name,
+            kms_key,
+            experiment_config,
+        )
+
+
+class _AnalysisConfigGenerator:
+    """Creates analysis_config objects for different type of runs."""
+
+    @classmethod
+    def explainability(
+        cls,
+        data_config: DataConfig,
+        model_config: ModelConfig,
+        model_scores: ModelPredictedLabelConfig,
+        explainability_config: ExplainabilityConfig,
+    ):
+        """Generates a config for Explainability"""
         analysis_config = data_config.get_config()
         predictor_config = model_config.get_predictor_config()
         if isinstance(model_scores, ModelPredictedLabelConfig):
@@ -1406,20 +1418,84 @@ def run_explainability(
             explainability_methods = explainability_config.get_explainability_config()
         analysis_config["methods"] = explainability_methods
         analysis_config["predictor"] = predictor_config
-        if job_name is None:
-            if self.job_name_prefix:
-                job_name = utils.name_from_base(self.job_name_prefix)
-            else:
-                job_name = utils.name_from_base("Clarify-Explainability")
-        return self._run(
-            data_config,
-            analysis_config,
-            wait,
-            logs,
-            job_name,
-            kms_key,
-            experiment_config,
-        )
+        return cls._common(analysis_config)
+
+    @classmethod
+    def bias_pre_training(
+        cls, data_config: DataConfig, bias_config: BiasConfig, methods: Union[str, List[str]]
+    ):
+        """Generates a config for Bias Pre Training"""
+        analysis_config = {
+            **data_config.get_config(),
+            **bias_config.get_config(),
+            "methods": {"pre_training_bias": {"methods": methods}},
+        }
+        return cls._common(analysis_config)
+
+    @classmethod
+    def bias_post_training(
+        cls,
+        data_config: DataConfig,
+        bias_config: BiasConfig,
+        model_predicted_label_config: ModelPredictedLabelConfig,
+        methods: Union[str, List[str]],
+        model_config: ModelConfig,
+    ):
+        """Generates a config for Bias Post Training"""
+        analysis_config = {
+            **data_config.get_config(),
+            **bias_config.get_config(),
+            "predictor": {**model_config.get_predictor_config()},
+            "methods": {"post_training_bias": {"methods": methods}},
+        }
+        if model_predicted_label_config:
+            (
+                probability_threshold,
+                predictor_config,
+            ) = model_predicted_label_config.get_predictor_config()
+            if predictor_config:
+                analysis_config["predictor"].update(predictor_config)
+            _set(probability_threshold, "probability_threshold", analysis_config)
+        return cls._common(analysis_config)
+
+    @classmethod
+    def bias(
+        cls,
+        data_config: DataConfig,
+        bias_config: BiasConfig,
+        model_config: ModelConfig,
+        model_predicted_label_config: ModelPredictedLabelConfig,
+        pre_training_methods: Union[str, List[str]] = "all",
+        post_training_methods: Union[str, List[str]] = "all",
+    ):
+        """Generates a config for Bias"""
+        analysis_config = {
+            **data_config.get_config(),
+            **bias_config.get_config(),
+            "predictor": model_config.get_predictor_config(),
+            "methods": {
+                "pre_training_bias": {"methods": pre_training_methods},
+                "post_training_bias": {"methods": post_training_methods},
+            },
+        }
+        if model_predicted_label_config:
+            (
+                probability_threshold,
+                predictor_config,
+            ) = model_predicted_label_config.get_predictor_config()
+            if predictor_config:
+                analysis_config["predictor"].update(predictor_config)
+            _set(probability_threshold, "probability_threshold", analysis_config)
+        return cls._common(analysis_config)
+
+    @staticmethod
+    def _common(analysis_config):
+        """Extends analysis config with common values"""
+        analysis_config["methods"]["report"] = {
+            "name": "report",
+            "title": "Analysis Report",
+        }
+        return analysis_config
 
 
 def _upload_analysis_config(analysis_config_file, s3_output_path, sagemaker_session, kms_key):
diff --git a/tests/unit/test_clarify.py b/tests/unit/test_clarify.py
index fa437573f0..7375657944 100644
--- a/tests/unit/test_clarify.py
+++ b/tests/unit/test_clarify.py
@@ -29,6 +29,7 @@
     SHAPConfig,
     TextConfig,
     ImageConfig,
+    _AnalysisConfigGenerator,
 )
 
 JOB_NAME_PREFIX = "my-prefix"
@@ -764,7 +765,10 @@ def test_pre_training_bias(
             "label_values_or_threshold": [1],
             "facet": [{"name_or_index": "F1"}],
             "group_variable": "F2",
-            "methods": {"pre_training_bias": {"methods": "all"}},
+            "methods": {
+                "report": {"name": "report", "title": "Analysis Report"},
+                "pre_training_bias": {"methods": "all"},
+            },
         }
         mock_method.assert_called_with(
             data_config,
@@ -827,7 +831,10 @@ def test_post_training_bias(
             "joinsource_name_or_index": "F4",
             "facet": [{"name_or_index": "F1"}],
             "group_variable": "F2",
-            "methods": {"post_training_bias": {"methods": "all"}},
+            "methods": {
+                "report": {"name": "report", "title": "Analysis Report"},
+                "post_training_bias": {"methods": "all"},
+            },
             "predictor": {
                 "model_name": "xgboost-model",
                 "instance_type": "ml.c5.xlarge",
@@ -985,7 +992,10 @@ def _run_test_explain(
                 "grid_resolution": 20,
                 "top_k_features": 10,
             }
-        expected_analysis_config["methods"] = expected_explanation_configs
+        expected_analysis_config["methods"] = {
+            "report": {"name": "report", "title": "Analysis Report"},
+            **expected_explanation_configs,
+        }
         mock_method.assert_called_with(
             data_config,
             expected_analysis_config,
@@ -1277,3 +1287,128 @@ def test_shap_with_image_config(
         expected_predictor_config,
         expected_image_config=expected_image_config,
     )
+
+
+def test_analysis_config_generator_for_explainability(data_config, model_config):
+    model_scores = ModelPredictedLabelConfig(
+        probability="pr",
+        label_headers=["success"],
+    )
+    actual = _AnalysisConfigGenerator.explainability(
+        data_config,
+        model_config,
+        model_scores,
+        SHAPConfig(),
+    )
+    expected = {
+        "dataset_type": "text/csv",
+        "headers": ["Label", "F1", "F2", "F3", "F4"],
+        "joinsource_name_or_index": "F4",
+        "label": "Label",
+        "methods": {
+            "report": {"name": "report", "title": "Analysis Report"},
+            "shap": {"save_local_shap_values": True, "use_logit": False},
+        },
+        "predictor": {
+            "initial_instance_count": 1,
+            "instance_type": "ml.c5.xlarge",
+            "label_headers": ["success"],
+            "model_name": "xgboost-model",
+            "probability": "pr",
+        },
+    }
+    assert actual == expected
+
+
+def test_analysis_config_generator_for_bias_pre_training(data_config, data_bias_config):
+    actual = _AnalysisConfigGenerator.bias_pre_training(
+        data_config, data_bias_config, methods="all"
+    )
+    expected = {
+        "dataset_type": "text/csv",
+        "facet": [{"name_or_index": "F1"}],
+        "group_variable": "F2",
+        "headers": ["Label", "F1", "F2", "F3", "F4"],
+        "joinsource_name_or_index": "F4",
+        "label": "Label",
+        "label_values_or_threshold": [1],
+        "methods": {
+            "report": {"name": "report", "title": "Analysis Report"},
+            "pre_training_bias": {"methods": "all"},
+        },
+    }
+    assert actual == expected
+
+
+def test_analysis_config_generator_for_bias_post_training(
+    data_config, data_bias_config, model_config
+):
+    model_predicted_label_config = ModelPredictedLabelConfig(
+        probability="pr",
+        label_headers=["success"],
+    )
+    actual = _AnalysisConfigGenerator.bias_post_training(
+        data_config,
+        data_bias_config,
+        model_predicted_label_config,
+        methods="all",
+        model_config=model_config,
+    )
+    expected = {
+        "dataset_type": "text/csv",
+        "facet": [{"name_or_index": "F1"}],
+        "group_variable": "F2",
+        "headers": ["Label", "F1", "F2", "F3", "F4"],
+        "joinsource_name_or_index": "F4",
+        "label": "Label",
+        "label_values_or_threshold": [1],
+        "methods": {
+            "report": {"name": "report", "title": "Analysis Report"},
+            "post_training_bias": {"methods": "all"},
+        },
+        "predictor": {
+            "initial_instance_count": 1,
+            "instance_type": "ml.c5.xlarge",
+            "label_headers": ["success"],
+            "model_name": "xgboost-model",
+            "probability": "pr",
+        },
+    }
+    assert actual == expected
+
+
+def test_analysis_config_generator_for_bias(data_config, data_bias_config, model_config):
+    model_predicted_label_config = ModelPredictedLabelConfig(
+        probability="pr",
+        label_headers=["success"],
+    )
+    actual = _AnalysisConfigGenerator.bias(
+        data_config,
+        data_bias_config,
+        model_config,
+        model_predicted_label_config,
+        pre_training_methods="all",
+        post_training_methods="all",
+    )
+    expected = {
+        "dataset_type": "text/csv",
+        "facet": [{"name_or_index": "F1"}],
+        "group_variable": "F2",
+        "headers": ["Label", "F1", "F2", "F3", "F4"],
+        "joinsource_name_or_index": "F4",
+        "label": "Label",
+        "label_values_or_threshold": [1],
+        "methods": {
+            "report": {"name": "report", "title": "Analysis Report"},
+            "post_training_bias": {"methods": "all"},
+            "pre_training_bias": {"methods": "all"},
+        },
+        "predictor": {
+            "initial_instance_count": 1,
+            "instance_type": "ml.c5.xlarge",
+            "label_headers": ["success"],
+            "model_name": "xgboost-model",
+            "probability": "pr",
+        },
+    }
+    assert actual == expected