diff --git a/.build-constraints.txt b/.build-constraints.txt
index a1996e796..6a9aa29d1 100644
--- a/.build-constraints.txt
+++ b/.build-constraints.txt
@@ -1,9 +1,9 @@
 hatch-fancy-pypi-readme==25.1.0 \
     --hash=sha256:9c58ed3dff90d51f43414ce37009ad1d5b0f08ffc9fc216998a06380f01c0045 \
     --hash=sha256:ce0134c40d63d874ac48f48ccc678b8f3b62b8e50e9318520d2bffc752eedaf3
-hatchling==1.29.0 \
-    --hash=sha256:50af9343281f34785fab12da82e445ed987a6efb34fd8c2fc0f6e6630dbcc1b0 \
-    --hash=sha256:793c31816d952cee405b83488ce001c719f325d9cda69f1fc4cd750527640ea6
+hatchling==1.30.1 \
+    --hash=sha256:161eacafb3c6f91526e92116d21426369f2c36e98c36a864f11a96345ad4ee31 \
+    --hash=sha256:eee4fd45357f72ebb3d7a42e5d72cfb5e29ed426d79e8836288926c4258d5f2e
     # via hatch-fancy-pypi-readme
 packaging==26.2 \
     --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
@@ -17,7 +17,7 @@ pluggy==1.6.0 \
     --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \
     --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746
     # via hatchling
-trove-classifiers==2026.1.14.14 \
-    --hash=sha256:00492545a1402b09d4858605ba190ea33243d361e2b01c9c296ce06b5c3325f3 \
-    --hash=sha256:1f9553927f18d0513d8e5ff80ab8980b8202ce37ecae0e3274ed2ef11880e74d
+trove-classifiers==2026.6.1.19 \
+    --hash=sha256:ab4c4ec93cc4a4e7815fa759906e05e6bb3f2fbd92ea0f897288c6a43efd15b3 \
+    --hash=sha256:c5132b4b61a829d11cfbd2d72e97f20a45ed6edb95e45c5efdeb5e00836b2745
     # via hatchling
diff --git a/Makefile b/Makefile
index d500170a9..ac11e316a 100644
--- a/Makefile
+++ b/Makefile
@@ -279,7 +279,11 @@ lock-dependencies: ## Regenerate top-level uv.lock and .build-constraints.txt
 	$(UV_RUN) --group yq tomlq -r '.["build-system"].requires[]' pyproject.toml | \
 	  uv pip compile --generate-hashes --universal --no-header - > build-constraints-new.txt
 	mv build-constraints-new.txt .build-constraints.txt
-	perl -pi -e 's|registry = "https://[^"]*"|registry = "https://pypi.org/simple"|g' uv.lock
+	# Normalize the lock so contributors inside Databricks (private proxy) and outside (public PyPI)
+	# produce an identical file. A proxy mirrors PyPI with identical paths, so rewrite the registry
+	# index and every per-package "/packages/..." download URL to the public hosts. Also drop the
+	# "size" field: the private proxy never reports it, so it is the only form both can reproduce.
+	perl -pi -e 's|registry = "https://[^"]*"|registry = "https://pypi.org/simple"|g; s|url = "https://[^/"]+/packages/|url = "https://files.pythonhosted.org/packages/|g; s|, size = \d+||g' uv.lock
 
 ##@ Misc
 
diff --git a/pyproject.toml b/pyproject.toml
index edb73dfa7..01a92a26e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,6 +40,7 @@ dependencies = [
     "databricks-sdk~=0.73",
     "sqlalchemy>=2.0,<3.0",
     "PyYAML~=6.0.3",
+    "pydantic>=2.8.2,<3",
 ]
 
 [project.optional-dependencies]
@@ -884,6 +885,7 @@ redefining-builtins-modules = ["six.moves", "past.builtins", "future.builtins",
 "tests/integration/test_app_backend.py" = "import-outside-toplevel"
 "src/databricks/labs/dqx/anomaly/anomaly_workflow.py" = "import-outside-toplevel"
 "src/databricks/labs/dqx/check_funcs.py" = "protected-access"
+"tests/unit/test_engine.py" = "protected-access"
 "tests/unit/test_anomaly_llm_explainer.py" = "protected-access"
 "tests/unit/test_anomaly_check_funcs_validation.py" = "protected-access"
 "tests/unit/test_anomaly_scoring_run.py" = "protected-access"
diff --git a/src/databricks/labs/dqx/anomaly/core.py b/src/databricks/labs/dqx/anomaly/core.py
index 3e46acb19..46c659197 100644
--- a/src/databricks/labs/dqx/anomaly/core.py
+++ b/src/databricks/labs/dqx/anomaly/core.py
@@ -37,7 +37,6 @@
 logger = logging.getLogger(__name__)
 
 DEFAULT_SAMPLE_FRACTION = 0.3
-DEFAULT_MAX_ROWS = 1_000_000
 DEFAULT_TRAIN_RATIO = 0.8
 SCORE_QUANTILE_PROBS = [0.0, 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 1.0]
 SCORE_QUANTILE_KEYS = ["p00", "p01", "p05", "p10", "p25", "p50", "p75", "p90", "p95", "p99", "p100"]
diff --git a/src/databricks/labs/dqx/checks_serializer.py b/src/databricks/labs/dqx/checks_serializer.py
index df489dd26..5fb5bc495 100644
--- a/src/databricks/labs/dqx/checks_serializer.py
+++ b/src/databricks/labs/dqx/checks_serializer.py
@@ -254,7 +254,7 @@ def deserialize(self, checks: list[dict]) -> list[DQRule]:
             logger.debug(f"Processing check definition: {check_def}")
 
             check = check_def.get("check", {})
-            name = check_def.get("name", None)
+            name = check_def.get("name") or ""
             func_name = check.get("function")
             func = resolve_check_function(func_name, self.custom_checks, fail_on_missing=True)
             assert func  # should already be validated
diff --git a/src/databricks/labs/dqx/checks_validator.py b/src/databricks/labs/dqx/checks_validator.py
index 27d5e4c0c..e8941bc47 100644
--- a/src/databricks/labs/dqx/checks_validator.py
+++ b/src/databricks/labs/dqx/checks_validator.py
@@ -2,44 +2,48 @@
 import functools as ft
 import inspect
 from collections.abc import Callable
-from dataclasses import dataclass, field
 from types import UnionType
 from typing import Any, get_origin, get_args
 
+from pydantic import BaseModel, ConfigDict
+
 from databricks.labs.dqx.checks_resolver import resolve_check_function
 from databricks.labs.dqx.rule import Criticality
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass(frozen=True)
-class ChecksValidationStatus:
-    """Class to represent the validation status."""
+class ChecksValidationStatus(BaseModel):
+    """Class to represent the validation status.
+
+    This model is used as a mutable accumulator: *add_error* and *add_errors* append to the
+    *errors* list in place.  Pydantic instantiates a fresh copy of the ``[]`` default for each
+    instance, so the list is never shared between instances created via the constructor.  The
+    only sharing risk is a shallow ``model_copy()`` (without ``deep=True``); this model is never
+    shallow-copied, but use ``model_copy(deep=True)`` if that ever changes.
+    """
+
+    model_config = ConfigDict(extra="forbid")
 
-    _errors: list[str] = field(default_factory=list)
+    errors: list[str] = []
 
     def add_error(self, error: str):
         """Add an error to the validation status."""
-        self._errors.append(error)
+        self.errors.append(error)
 
     def add_errors(self, errors: list[str]):
-        """Add an error to the validation status."""
-        self._errors.extend(errors)
+        """Add errors to the validation status."""
+        self.errors.extend(errors)
 
     @property
     def has_errors(self) -> bool:
         """Check if there are any errors in the validation status."""
-        return bool(self._errors)
-
-    @property
-    def errors(self) -> list[str]:
-        """Get the list of errors in the validation status."""
-        return self._errors
+        return bool(self.errors)
 
     def to_string(self) -> str:
         """Convert the validation status to a string."""
         if self.has_errors:
-            return "\n".join(self._errors)
+            return "\n".join(self.errors)
         return "No errors found"
 
     def __str__(self) -> str:
diff --git a/src/databricks/labs/dqx/config.py b/src/databricks/labs/dqx/config.py
index d396a4462..b4a4216d5 100644
--- a/src/databricks/labs/dqx/config.py
+++ b/src/databricks/labs/dqx/config.py
@@ -1,6 +1,8 @@
-import abc
+from abc import ABC
 from dataclasses import asdict, dataclass, field
-from functools import cached_property
+from typing import Any
+
+from pydantic import BaseModel, ValidationError, model_validator
 
 from databricks.labs.dqx.checks_serializer import SerializerFactory
 from databricks.labs.dqx.errors import InvalidConfigError, InvalidParameterError
@@ -326,8 +328,7 @@ def get_run_config(self, run_config_name: str | None = "default") -> RunConfig:
         raise InvalidConfigError("No run configurations available")
 
 
-@dataclass
-class BaseChecksStorageConfig(abc.ABC):
+class BaseChecksStorageConfig(BaseModel, ABC):
     """Marker base class for storage configuration.
 
     Args:
@@ -336,8 +337,32 @@ class BaseChecksStorageConfig(abc.ABC):
 
     location: str
 
+    def __init__(self, **data: Any) -> None:
+        try:
+            super().__init__(**data)
+        except ValidationError as exc:
+            raise InvalidConfigError(str(exc)) from exc
+
+    def replace(self, **changes: Any) -> "BaseChecksStorageConfig":
+        """Return a new config instance with the given field overrides, fully re-validated.
+
+        Unlike *model_copy(update=...)*, which shallow-copies the instance and skips all
+        validators, this rebuilds the config through the constructor so the *model_validator*
+        checks (e.g. *mode* and *location* format) re-run against the updated fields. Use this
+        instead of *model_copy* when overriding fields, so an invalid override is rejected at
+        the point of the change rather than failing later during a save/load operation.
+
+        Args:
+            **changes: Field values to override on the new instance.
+
+        Returns:
+            A new, fully validated config of the same concrete type.
+        """
+        fields = {name: getattr(self, name) for name in type(self).model_fields}
+        fields.update(changes)
+        return type(self)(**fields)
+
 
-@dataclass
 class FileChecksStorageConfig(BaseChecksStorageConfig):
     """
     Configuration class for storing checks in a file.
@@ -346,14 +371,20 @@ class FileChecksStorageConfig(BaseChecksStorageConfig):
         location: The file path where the checks are stored.
     """
 
-    location: str
-
-    def __post_init__(self):
-        if not self.location:
+    @model_validator(mode='before')
+    @classmethod
+    def validate_location(cls, data: Any) -> Any:
+        # `cls is FileChecksStorageConfig` intentionally excludes subclasses.
+        # InstallationChecksStorageConfig uses diamond inheritance from this class and
+        # WorkspaceFileChecksStorageConfig; both validators are inherited and would fire
+        # for any subclass without the exact-class guard, giving confusing duplicate errors.
+        # InstallationChecksStorageConfig has its own validate_installation_location that
+        # covers the empty-location case for itself.
+        if cls is FileChecksStorageConfig and isinstance(data, dict) and not data.get("location"):
             raise InvalidConfigError("The file path ('location' field) must not be empty or None.")
+        return data
 
 
-@dataclass
 class WorkspaceFileChecksStorageConfig(BaseChecksStorageConfig):
     """
     Configuration class for storing checks in a workspace file.
@@ -362,14 +393,15 @@ class WorkspaceFileChecksStorageConfig(BaseChecksStorageConfig):
         location: The workspace file path where the checks are stored.
     """
 
-    location: str
-
-    def __post_init__(self):
-        if not self.location:
+    @model_validator(mode='before')
+    @classmethod
+    def validate_location(cls, data: Any) -> Any:
+        # See FileChecksStorageConfig.validate_location for why the exact-class guard is needed.
+        if cls is WorkspaceFileChecksStorageConfig and isinstance(data, dict) and not data.get("location"):
             raise InvalidConfigError("The workspace file path ('location' field) must not be empty or None.")
+        return data
 
 
-@dataclass
 class TableChecksStorageConfig(BaseChecksStorageConfig):
     """
     Configuration class for storing checks in a table.
@@ -387,17 +419,19 @@ class TableChecksStorageConfig(BaseChecksStorageConfig):
             When None (default), loads the latest batch.
     """
 
-    location: str
     run_config_name: str = "default"  # to filter checks by run config
     mode: str = "append"
     rule_set_fingerprint: str | None = None  # to filter checks by rule set fingerprint
 
-    def __post_init__(self):
-        if not self.location:
+    @model_validator(mode='before')
+    @classmethod
+    def validate_location(cls, data: Any) -> Any:
+        # See FileChecksStorageConfig.validate_location for why the exact-class guard is needed.
+        if cls is TableChecksStorageConfig and isinstance(data, dict) and not data.get("location"):
             raise InvalidConfigError("The table name ('location' field) must not be empty or None.")
+        return data
 
 
-@dataclass
 class LakebaseChecksStorageConfig(BaseChecksStorageConfig):
     """
     Configuration class for storing checks in a Lakebase table.
@@ -418,7 +452,6 @@ class LakebaseChecksStorageConfig(BaseChecksStorageConfig):
             When None (default), loads the latest batch.
     """
 
-    location: str
     instance_name: str | None = None
     client_id: str | None = None
     port: str = "5432"
@@ -426,11 +459,19 @@ class LakebaseChecksStorageConfig(BaseChecksStorageConfig):
     mode: str = "append"
     rule_set_fingerprint: str | None = None
 
-    def __post_init__(self):
-        if not self.location or self.location == "":
+    @model_validator(mode='before')
+    @classmethod
+    def validate_location_present(cls, data: Any) -> Any:
+        if cls is LakebaseChecksStorageConfig and isinstance(data, dict) and not data.get("location"):
             raise InvalidParameterError("Location must not be empty or None.")
+        return data
+
+    @model_validator(mode='after')
+    def validate_lakebase_config(self) -> 'LakebaseChecksStorageConfig':
+        if self.__class__ is not LakebaseChecksStorageConfig:
+            return self
 
-        if not self.instance_name or self.instance_name == "":
+        if not self.instance_name:
             raise InvalidParameterError("Instance name must not be empty or None.")
 
         if len(self.location.split(".")) != 3:
@@ -441,26 +482,27 @@ def __post_init__(self):
         if self.mode not in ("append", "overwrite"):
             raise InvalidConfigError(f"Invalid mode '{self.mode}'. Must be 'append' or 'overwrite'.")
 
+        return self
+
     def _split_location(self) -> tuple[str, ...]:
         """Splits 'database.schema.table' into components."""
         if not self.location:
             raise InvalidConfigError("location must be set before accessing database components.")
         return tuple(self.location.split("."))
 
-    @cached_property
+    @property
     def database_name(self) -> str:
         return self._split_location()[0]
 
-    @cached_property
+    @property
     def schema_name(self) -> str:
         return self._split_location()[1]
 
-    @cached_property
+    @property
     def table_name(self) -> str:
         return self._split_location()[2]
 
 
-@dataclass
 class VolumeFileChecksStorageConfig(BaseChecksStorageConfig):
     """
     Configuration class for storing checks in a Unity Catalog volume file.
@@ -469,13 +511,19 @@ class VolumeFileChecksStorageConfig(BaseChecksStorageConfig):
         location: The Unity Catalog volume file path where the checks are stored.
     """
 
-    location: str
-
-    def __post_init__(self):
-        if not self.location:
+    @model_validator(mode='before')
+    @classmethod
+    def validate_location_present(cls, data: Any) -> Any:
+        if cls is VolumeFileChecksStorageConfig and isinstance(data, dict) and not data.get("location"):
             raise InvalidParameterError(
                 "The Unity Catalog volume file path ('location' field) must not be empty or None."
             )
+        return data
+
+    @model_validator(mode='after')
+    def validate_location(self) -> 'VolumeFileChecksStorageConfig':
+        if self.__class__ is not VolumeFileChecksStorageConfig:
+            return self
 
         # Expected format: /Volumes/{catalog}/{schema}/{volume}/{path/to/file}
         if not self.location.startswith("/Volumes/"):
@@ -492,8 +540,9 @@ def __post_init__(self):
         if len(parts) < 6 or not parts[-1].lower().endswith(SerializerFactory.get_supported_extensions()):
             raise InvalidParameterError("Invalid path: Path must include a file name after the volume")
 
+        return self
+
 
-@dataclass
 class InstallationChecksStorageConfig(
     WorkspaceFileChecksStorageConfig,
     TableChecksStorageConfig,
@@ -523,3 +572,11 @@ class InstallationChecksStorageConfig(
     assume_user: bool = True
     install_folder: str | None = None
     overwrite_location: bool = False
+
+    @model_validator(mode='before')
+    @classmethod
+    def validate_installation_location(cls, data: Any) -> Any:
+        # Only guard against an explicitly empty/None location; an absent key keeps the default above.
+        if isinstance(data, dict) and "location" in data and not data["location"]:
+            raise InvalidConfigError("The workspace file path ('location' field) must not be empty or None.")
+        return data
diff --git a/src/databricks/labs/dqx/engine.py b/src/databricks/labs/dqx/engine.py
index 835b246a8..42e72be92 100644
--- a/src/databricks/labs/dqx/engine.py
+++ b/src/databricks/labs/dqx/engine.py
@@ -5,7 +5,6 @@
 import re
 from concurrent import futures
 from collections.abc import Callable
-from dataclasses import replace
 from datetime import datetime
 from functools import cached_property
 from typing import Any
@@ -484,7 +483,7 @@ def _preselect_original_columns(self, df: DataFrame, check: DQRule) -> DQRule:
         # preselect original columns
         rule_kwargs = check.check_func_kwargs.copy()
         rule_kwargs["columns"] = [col for col in df.columns if col not in set(self._result_column_names.values())]
-        return replace(check, check_func_kwargs=rule_kwargs)
+        return check.replace(check_func_kwargs=rule_kwargs)
 
     def _append_empty_checks(self, df: DataFrame) -> DataFrame:
         """Append empty checks at the end of DataFrame.
diff --git a/src/databricks/labs/dqx/installer/logs.py b/src/databricks/labs/dqx/installer/logs.py
index 2822d5027..f67b63500 100644
--- a/src/databricks/labs/dqx/installer/logs.py
+++ b/src/databricks/labs/dqx/installer/logs.py
@@ -20,18 +20,6 @@
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class LogRecord:
-    timestamp: int
-    job_id: int
-    job_name: str
-    task_name: str
-    job_run_id: int
-    level: str
-    component: str
-    message: str
-
-
 @dataclass
 class PartialLogRecord:
     """The information found within a log file record."""
diff --git a/src/databricks/labs/dqx/rule.py b/src/databricks/labs/dqx/rule.py
index 13220333b..166575d04 100644
--- a/src/databricks/labs/dqx/rule.py
+++ b/src/databricks/labs/dqx/rule.py
@@ -4,14 +4,15 @@
 import json
 import logging
 from enum import Enum
-from dataclasses import dataclass, field
 import functools as ft
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Annotated, Any, ClassVar
 
+from pydantic import BaseModel, ConfigDict, ValidationError, model_validator
+from pydantic.json_schema import WithJsonSchema
 from pyspark.sql import Column
 import pyspark.sql.functions as F
-from databricks.labs.dqx.utils import get_column_name_or_alias, normalize_bound_args
+from databricks.labs.dqx.utils import get_column_name_or_alias, normalize_bound_args, SparkColumn
 from databricks.labs.dqx.errors import InvalidCheckError, InvalidParameterError
 
 logger = logging.getLogger(__name__)
@@ -167,8 +168,8 @@ def _build_columns_args(self, columns: list[str | Column] | None, valid_params:
 
 
 class DQRuleTypeMixin:
-    _expected_rule_type: str  # to be defined in subclasses
-    _alternative_rules: list[str]  # e.g., "DQRowRule" or "DQDatasetRule"
+    _expected_rule_type: ClassVar[str]  # to be defined in subclasses
+    _alternative_rules: ClassVar[list[str]]  # e.g., "DQRowRule" or "DQDatasetRule"
 
     def _validate_rule_type(self, check_func: Callable) -> None:
         """
@@ -201,8 +202,7 @@ def _determine_rule_type(self, check_func: Callable) -> str | None:
         return CHECK_FUNC_REGISTRY.get(check_func.__name__, None)  # default to None
 
 
-@dataclass(frozen=True)
-class DQRule(abc.ABC, DQRuleTypeMixin, SingleColumnMixin, MultipleColumnsMixin):
+class DQRule(BaseModel, abc.ABC, DQRuleTypeMixin, SingleColumnMixin, MultipleColumnsMixin):
     """Represents a data quality rule that applies a quality check function to column(s) or
     column expression(s). This class includes the following attributes:
     * *check_func* - The function used to perform the quality check.
@@ -224,26 +224,81 @@ class DQRule(abc.ABC, DQRuleTypeMixin, SingleColumnMixin, MultipleColumnsMixin):
         rules generated from a ``DQForEachColRule``.
     """
 
-    check_func: Callable
+    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
+
+    check_func: Annotated[Callable, WithJsonSchema({"type": "string"})]
     name: str = ""
     criticality: str = Criticality.ERROR.value
-    column: str | Column | None = None
-    columns: list[str | Column] | None = None  # some checks require list of columns instead of column
+    column: str | SparkColumn | None = None
+    columns: list[str | SparkColumn] | None = None  # some checks require list of columns instead of column
     filter: str | None = None
-    check_func_args: list[Any] = field(default_factory=list)
-    check_func_kwargs: dict[str, Any] = field(default_factory=dict)
+    check_func_args: list[Any] = []
+    check_func_kwargs: dict[str, Any] = {}
     user_metadata: dict[str, str] | None = None
-    message_expr: str | Column | None = None
+    message_expr: str | SparkColumn | None = None
+
+    def __init__(self, **data: Any) -> None:
+        try:
+            super().__init__(**data)
+        except ValidationError as exc:
+            raise InvalidCheckError(str(exc)) from exc
+
+    @model_validator(mode='before')
+    @classmethod
+    def _reject_none_columns(cls, data: Any) -> Any:
+        """Raise the DQX-specific error for None elements in *columns* before Pydantic type validation.
+
+        Pydantic would otherwise reject a None element with a generic ValidationError; callers expect
+        InvalidCheckError. Runs before field coercion so the raw input list is inspected as provided.
+
+        Args:
+            data: Raw input passed to the model (a mapping of field values when constructed with keywords).
+
+        Returns:
+            The unmodified input data.
+
+        Raises:
+            InvalidCheckError: If *columns* is a list containing a None element.
+        """
+        if isinstance(data, dict):
+            columns = data.get("columns")
+            if isinstance(columns, list) and any(column is None for column in columns):
+                raise InvalidCheckError("'columns' list contains a None element.")
+        return data
+
+    @model_validator(mode='before')
+    @classmethod
+    def _populate_columns_from_kwargs(cls, data: Any) -> Any:
+        """Promote column/columns from check_func_kwargs into top-level fields when not set directly.
+
+        Running this as a mode='before' validator means the promoted values go through the same
+        SparkColumn/type coercion as if the caller had passed them explicitly, without needing
+        object.__setattr__ on the frozen model.
+
+        Args:
+            data: Raw input mapping before field coercion.
 
-    def __post_init__(self):
+        Returns:
+            Possibly updated input mapping with column/columns set from check_func_kwargs.
+        """
+        if not isinstance(data, dict):
+            return data
+        check_func_kwargs: dict = data.get("check_func_kwargs") or {}
+        if data.get("column") is None and "column" in check_func_kwargs:
+            data = {**data, "column": check_func_kwargs["column"]}
+        if data.get("columns") is None and "columns" in check_func_kwargs:
+            data = {**data, "columns": check_func_kwargs["columns"]}
+        return data
+
+    @model_validator(mode='after')
+    def _validate_and_initialize(self) -> 'DQRule':
         self._validate_rule_type(self.check_func)
-        self._initialize_column_if_missing()
-        self._initialize_columns_if_missing()
         self._validate_attributes()
         check_condition = self.get_check_condition()
         self._initialize_name_if_missing(check_condition)
         if isinstance(self.message_expr, str):
             self._validate_message_expression(self.message_expr)
+        return self
 
     @abc.abstractmethod
     def get_check_condition(self) -> Column:
@@ -279,6 +334,31 @@ def prepare_check_func_args_and_kwargs(self) -> tuple[list, dict]:
 
         return args, kwargs
 
+    def replace(self, **changes: Any) -> "DQRule":
+        """Return a new rule instance with the given field overrides.
+
+        Unlike *model_copy*, this rebuilds the instance through the constructor so validation
+        re-runs and *functools.cached_property* derived state (e.g. *rule_fingerprint*,
+        *columns_as_string_expr*) is recomputed from the updated fields rather than copied stale.
+        *model_copy(update=...)* shallow-copies the instance dict, which carries over the already
+        cached values and would therefore ignore the updated fields.
+
+        Note that regular fields are copied verbatim and only re-validated, not re-derived: an
+        already-populated *name* is preserved as-is (it is not regenerated from a changed
+        *column*/*columns*), and a *column*/*columns* already promoted from *check_func_kwargs*
+        is not re-promoted. Current callers only override *check_func_kwargs*, for which this is
+        the intended behaviour.
+
+        Args:
+            **changes: Field values to override on the new instance.
+
+        Returns:
+            A new, fully validated rule of the same concrete type.
+        """
+        fields = {name: getattr(self, name) for name in type(self).model_fields}
+        fields.update(changes)
+        return type(self)(**fields)
+
     @ft.cached_property
     def rule_fingerprint(self) -> str:
         """Compute a deterministic SHA-256 hash of a single rule definition.
@@ -327,22 +407,14 @@ def to_dict(self) -> dict:
             logger.warning("Message expressions of type 'Column' cannot be serialized; falling back to default message")
         return metadata
 
-    def _initialize_column_if_missing(self):
-        """Handle scenarios where 'column' is provided in check_func_kwargs but not as an attribute."""
-        if "column" in self.check_func_kwargs:
-            if self.column is None:
-                object.__setattr__(self, "column", self.check_func_kwargs.get("column"))
-
-    def _initialize_columns_if_missing(self):
-        """Handle scenarios where 'columns' is provided in check_func_kwargs but not as an attribute."""
-        if "columns" in self.check_func_kwargs:
-            if self.columns is None:
-                object.__setattr__(self, "columns", self.check_func_kwargs.get("columns"))
-
     def _initialize_name_if_missing(self, check_condition: Column):
         """If name not provided directly, update it based on the condition."""
         if not self.name:
             normalized_name = get_column_name_or_alias(check_condition, normalize=True)
+            # object.__setattr__ is Pydantic's recommended way to write a field on a frozen
+            # model from within a mode='after' validator.  This cannot be promoted to a
+            # mode='before' validator because the name is derived from the computed check
+            # condition, which requires the model to be fully initialised first.
             object.__setattr__(self, "name", normalized_name)
 
     def _validate_attributes(self) -> None:
@@ -426,15 +498,14 @@ def _validate_message_expression(message_expr: str) -> None:
             ) from exc
 
 
-@dataclass(frozen=True)
 class DQRowRule(DQRule):
     """
     Represents a row-level data quality rule that applies a quality check function to a column or column expression.
     Works with check functions that take a single column or no column as input.
     """
 
-    _expected_rule_type: str = "row"
-    _alternative_rules: list[str] = field(default_factory=lambda: ["DQDatasetRule"])
+    _expected_rule_type: ClassVar[str] = "row"
+    _alternative_rules: ClassVar[list[str]] = ["DQDatasetRule"]
 
     def get_check_condition(self) -> Column:
         """
@@ -453,7 +524,6 @@ def check(self) -> Column:
         return condition
 
 
-@dataclass(frozen=True)
 class DQDatasetRule(DQRule):
     """
     Represents a dataset-level data quality rule that applies a quality check function to a column or
@@ -462,8 +532,8 @@ class DQDatasetRule(DQRule):
     rather than individual rows. Failed checks are appended to the result columns in the same way as row-level rules.
     """
 
-    _expected_rule_type: str = "dataset"
-    _alternative_rules: list[str] = field(default_factory=lambda: ["DQRowRule"])
+    _expected_rule_type: ClassVar[str] = "dataset"
+    _alternative_rules: ClassVar[list[str]] = ["DQRowRule"]
 
     def get_check_condition(self) -> Column:
         """
@@ -487,8 +557,7 @@ def check(self) -> tuple[Column, Callable, str | None]:
         return condition, apply_func, None
 
 
-@dataclass(frozen=True)
-class DQForEachColRule(DQRuleTypeMixin):
+class DQForEachColRule(BaseModel, DQRuleTypeMixin):
     """Represents a data quality rule that applies to a quality check function
     repeatedly on each specified column of the provided list of columns.
     This class includes the following attributes:
@@ -504,15 +573,23 @@ class DQForEachColRule(DQRuleTypeMixin):
     * *user_metadata* (optional) - User-defined key-value pairs added to metadata generated by the check.
     """
 
-    columns: list[str | Column | list[str | Column]]
-    check_func: Callable
+    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
+
+    columns: list[str | SparkColumn | list[str | SparkColumn]]
+    check_func: Annotated[Callable, WithJsonSchema({"type": "string"})]
     name: str = ""
     criticality: str = Criticality.ERROR.value
     filter: str | None = None
-    check_func_args: list[Any] = field(default_factory=list)
-    check_func_kwargs: dict[str, Any] = field(default_factory=dict)
+    check_func_args: list[Any] = []
+    check_func_kwargs: dict[str, Any] = {}
     user_metadata: dict[str, str] | None = None
-    message_expr: str | Column | None = None
+    message_expr: str | SparkColumn | None = None
+
+    def __init__(self, **data: Any) -> None:
+        try:
+            super().__init__(**data)
+        except ValidationError as exc:
+            raise InvalidCheckError(str(exc)) from exc
 
     def get_rules(self) -> list[DQRule]:
         """Build a list of rules for a set of columns.
diff --git a/src/databricks/labs/dqx/utils.py b/src/databricks/labs/dqx/utils.py
index 5e3a0ba92..645ed6dda 100644
--- a/src/databricks/labs/dqx/utils.py
+++ b/src/databricks/labs/dqx/utils.py
@@ -6,13 +6,13 @@
 from decimal import Decimal
 from enum import Enum
 from importlib.util import find_spec
-from typing import Any, TypeVar, overload
+from typing import Any, TypeVar, overload, Annotated
 from fnmatch import fnmatch
 from pathlib import Path
 
-
 from pyspark.sql import Column
 from pyspark.sql.types import StructType
+import pyspark.sql.functions as F
 
 # Import spark connect column if spark session is created using spark connect
 try:
@@ -20,13 +20,36 @@
 except ImportError:
     ConnectColumn = None  # type: ignore
 
-import pyspark.sql.functions as F
+from pydantic.functional_validators import PlainValidator
+from pydantic.json_schema import WithJsonSchema
 from databricks.sdk import WorkspaceClient
 from databricks.labs.blueprint.limiter import rate_limited
 from databricks.labs.dqx.errors import InvalidParameterError
 from databricks.labs.dqx.table_manager import SparkTableDataProvider
 from databricks.sdk.errors import NotFound
 
+
+def _validate_spark_column(value: Any) -> Any:
+    """Accept both classic pyspark.sql.Column and the Spark Connect variant.
+
+    In environments using Spark Connect (e.g. Databricks serverless / Python 3.10–3.11 CI),
+    ``F.col()`` / ``F.lit()`` return ``pyspark.sql.connect.column.Column``, which is not an
+    ``isinstance`` of the classic ``pyspark.sql.Column``.  Reading ConnectColumn at call time
+    (not at import time) means monkeypatching in tests works correctly.
+    """
+    _valid_types: tuple = (Column,) if ConnectColumn is None else (Column, ConnectColumn)
+    if isinstance(value, _valid_types):
+        return value
+    raise ValueError(f"Expected a PySpark Column, got {type(value).__name__}")
+
+
+SparkColumn = Annotated[
+    Column,
+    PlainValidator(_validate_spark_column),
+    WithJsonSchema({"type": "string"}),
+]
+"""Pydantic-compatible Column type that accepts both classic and Spark Connect columns."""
+
 logger = logging.getLogger(__name__)
 
 
diff --git a/tests/integration/test_save_and_load_checks_from_lakebase_table.py b/tests/integration/test_save_and_load_checks_from_lakebase_table.py
index 6849a7d25..85287603f 100644
--- a/tests/integration/test_save_and_load_checks_from_lakebase_table.py
+++ b/tests/integration/test_save_and_load_checks_from_lakebase_table.py
@@ -1,4 +1,3 @@
-import dataclasses
 import logging
 import os
 import re
@@ -578,8 +577,8 @@ def test_save_append_then_overwrite_same_run_config(ws, spark, make_lakebase_ins
 
     config = LakebaseChecksStorageConfig(location=location, client_id=lakebase_client_id, instance_name=instance.name)
 
-    dq_engine.save_checks(checks=TEST_CHECKS[:1], config=dataclasses.replace(config, mode="append"))
-    dq_engine.save_checks(checks=TEST_CHECKS[1:], config=dataclasses.replace(config, mode="overwrite"))
+    dq_engine.save_checks(checks=TEST_CHECKS[:1], config=config.replace(mode="append"))
+    dq_engine.save_checks(checks=TEST_CHECKS[1:], config=config.replace(mode="overwrite"))
 
     checks = dq_engine.load_checks(config=config)
     compare_checks(checks, TEST_CHECKS[1:])
diff --git a/tests/integration/test_save_and_load_checks_from_table.py b/tests/integration/test_save_and_load_checks_from_table.py
index 5f77ab168..ecfd9fa8f 100644
--- a/tests/integration/test_save_and_load_checks_from_table.py
+++ b/tests/integration/test_save_and_load_checks_from_table.py
@@ -1,6 +1,5 @@
 import json
 import time
-from dataclasses import dataclass
 from decimal import Decimal
 
 import pytest
@@ -558,9 +557,13 @@ def test_save_load_checks_from_table_in_user_installation(ws, installation_ctx,
     assert EXPECTED_CHECKS_FROM_TABLE_LOAD == checks, "Checks were not saved correctly"
 
 
-@dataclass
 class ChecksDummyStorageConfig(BaseChecksStorageConfig):
-    """Dummy storage config for testing unsupported storage type."""
+    """Dummy storage config for testing unsupported storage type.
+
+    BaseChecksStorageConfig is a Pydantic model, so this subclass must not be decorated with
+    @dataclass — doing so generates a conflicting __init__ that shadows Pydantic's and breaks
+    construction. A plain Pydantic subclass with a default *location* is sufficient.
+    """
 
     location: str = "test_location"
 
diff --git a/tests/unit/test_app_backend.py b/tests/unit/test_app_backend.py
index 69dea0c9f..674a9fc85 100644
--- a/tests/unit/test_app_backend.py
+++ b/tests/unit/test_app_backend.py
@@ -1870,7 +1870,7 @@ def test_submit_dry_run_raises_400_on_invalid_checks(
         mock_settings_svc: AppSettingsService,
     ) -> None:
         """submit_dry_run should raise HTTP 400 when check validation reports errors."""
-        validation = ChecksValidationStatus(_errors=["Unknown function: bad_func"])
+        validation = ChecksValidationStatus(errors=["Unknown function: bad_func"])
         body = DryRunIn(table_fqn="cat.sch.tbl", checks=_SAMPLE_CHECKS)
 
         app_conf = AppConfig(catalog="cat", schema_name="sch", job_id="")
diff --git a/tests/unit/test_build_rules.py b/tests/unit/test_build_rules.py
index 058145b1d..97aa48442 100644
--- a/tests/unit/test_build_rules.py
+++ b/tests/unit/test_build_rules.py
@@ -7,6 +7,7 @@
 import pytest
 import pyspark.sql.functions as F
 from pyspark.sql import Column
+import databricks.labs.dqx.utils as dqx_utils
 from databricks.labs.dqx.check_funcs import (
     is_not_null,
     is_not_null_and_not_empty,
@@ -1992,6 +1993,97 @@ def test_dq_dataset_rule_rule_fingerprint():
     assert all(char in "0123456789abcdef" for char in fingerprint)
 
 
+# ---------------------------------------------------------------------------
+# Regression: Connect-column acceptance (Task 2)
+# ---------------------------------------------------------------------------
+
+
+def test_spark_column_annotation_accepts_connect_column_type(monkeypatch):
+    """SparkColumn validator must accept objects whose type is ConnectColumn even when
+    it is NOT an instance of the classic pyspark.sql.Column.
+
+    In CI (Python 3.10/3.11) F.lit()/F.col() return pyspark.sql.connect.column.Column
+    which is a *different* class from pyspark.sql.Column.  Before the SparkColumn fix,
+    those environments raised pydantic_core.ValidationError: 'Input should be an instance
+    of Column'.  This regression simulates that CI environment deterministically via
+    monkeypatching — _validate_spark_column reads ConnectColumn at call time, so the patch
+    takes effect immediately for all rule constructors.
+    """
+
+    class FakeConnectColumn:
+        """Simulates pyspark.sql.connect.column.Column — not a subclass of classic Column."""
+
+    monkeypatch.setattr(dqx_utils, "ConnectColumn", FakeConnectColumn)
+
+    fake_col = FakeConnectColumn()
+    rule = DQRowRule(check_func=is_not_null, column="id", message_expr=fake_col)
+    assert rule.message_expr is fake_col
+
+
+def test_spark_column_annotation_rejects_non_column():
+    """SparkColumn validator must reject integers and other non-Column objects, raising
+    InvalidCheckError (wrapped from pydantic ValidationError by DQRule.__init__)."""
+    with pytest.raises(InvalidCheckError):
+        DQRowRule(check_func=is_not_null, column=42)  # int is not a Column or str
+
+
+# ---------------------------------------------------------------------------
+# json_schema generation (Task 3)
+# ---------------------------------------------------------------------------
+
+
+def test_dq_row_rule_model_json_schema():
+    """DQRowRule.model_json_schema() must not raise PydanticInvalidForJsonSchema.
+
+    Before the fix, Callable and Column fields caused: 'Cannot generate a JsonSchema for
+    core_schema.CallableSchema / IsInstanceSchema'.  WithJsonSchema annotations on check_func
+    and SparkColumn's __get_pydantic_json_schema__ resolve this.
+    """
+    schema = DQRowRule.model_json_schema()
+    assert isinstance(schema, dict)
+    assert "properties" in schema
+
+
+def test_dq_dataset_rule_model_json_schema():
+    """DQDatasetRule.model_json_schema() must not raise."""
+    schema = DQDatasetRule.model_json_schema()
+    assert isinstance(schema, dict)
+
+
+def test_dq_for_each_col_rule_model_json_schema():
+    """DQForEachColRule.model_json_schema() must not raise."""
+
+    schema = DQForEachColRule.model_json_schema()
+    assert isinstance(schema, dict)
+
+
+# ---------------------------------------------------------------------------
+# ValidationError → InvalidCheckError wrapping (Task 4)
+# ---------------------------------------------------------------------------
+
+
+def test_dq_row_rule_wrong_type_raises_invalid_check_error():
+    """Constructing DQRowRule with a wrong-typed field must raise InvalidCheckError, not the
+    raw pydantic_core.ValidationError.  DQX callers should never need to import pydantic to
+    handle construction errors from rule models.
+    """
+    with pytest.raises(InvalidCheckError):
+        DQRowRule(check_func=is_not_null, column="id", criticality=12345)  # int where str expected
+
+
+def test_dq_dataset_rule_wrong_type_raises_invalid_check_error():
+    """Same contract for DQDatasetRule."""
+    with pytest.raises(InvalidCheckError):
+        DQDatasetRule(check_func=is_unique, columns="not_a_list")  # str where list expected
+
+
+def test_dq_for_each_col_rule_wrong_type_raises_invalid_check_error():
+    """DQForEachColRule must wrap pydantic ValidationError into InvalidCheckError too, so all
+    public rule-construction entry points raise the same DQX error type for bad input."""
+    with pytest.raises(InvalidCheckError):
+        DQForEachColRule(check_func=is_not_null, columns=42)  # int where list expected
+
+
 def test_requires_dbr_version_sets_attribute():
     @requires_dbr_version("15.0")
     def my_check():
diff --git a/tests/unit/test_config.py b/tests/unit/test_config.py
index 1ec7842bd..dfb317c5e 100644
--- a/tests/unit/test_config.py
+++ b/tests/unit/test_config.py
@@ -2,6 +2,7 @@
 from databricks.labs.dqx.config import (
     WorkspaceConfig,
     RunConfig,
+    BaseChecksStorageConfig,
     InstallationChecksStorageConfig,
     FileChecksStorageConfig,
     LakebaseChecksStorageConfig,
@@ -553,3 +554,85 @@ def test_lakebase_checks_storage_config_with_custom_run_config_and_fingerprint()
     assert config.instance_name == "my_instance"
     assert config.run_config_name == "prod"
     assert config.rule_set_fingerprint == rule_set_fingerprint
+
+
+@pytest.mark.parametrize(
+    "config,changes,changed_field,expected_value,preserved_field,preserved_value",
+    [
+        (
+            FileChecksStorageConfig(location="/p/checks.yml"),
+            {"location": "/p/other.yml"},
+            "location",
+            "/p/other.yml",
+            None,
+            None,
+        ),
+        (
+            WorkspaceFileChecksStorageConfig(location="/Workspace/checks.yml"),
+            {"location": "/Workspace/other.yml"},
+            "location",
+            "/Workspace/other.yml",
+            None,
+            None,
+        ),
+        (
+            TableChecksStorageConfig(location="cat.sch.tbl", mode="append", run_config_name="rc"),
+            {"mode": "overwrite"},
+            "mode",
+            "overwrite",
+            "run_config_name",
+            "rc",
+        ),
+        (
+            VolumeFileChecksStorageConfig(location="/Volumes/c/s/v/checks.yml"),
+            {"location": "/Volumes/c/s/v/other.yml"},
+            "location",
+            "/Volumes/c/s/v/other.yml",
+            None,
+            None,
+        ),
+        (
+            InstallationChecksStorageConfig(run_config_name="rc", product_name="dqx"),
+            {"run_config_name": "rc2"},
+            "run_config_name",
+            "rc2",
+            "product_name",
+            "dqx",
+        ),
+    ],
+)
+def test_base_storage_config_replace_returns_new_same_type_with_overrides(
+    config, changes, changed_field, expected_value, preserved_field, preserved_value
+):
+    """`replace()` works for every storage-config subclass (incl. the diamond-inheritance
+    `InstallationChecksStorageConfig`): it returns a new instance of the same concrete type with the
+    override applied, other fields preserved, and the original (frozen) instance untouched."""
+    original_changed_value = getattr(config, changed_field)
+
+    replaced = config.replace(**changes)
+
+    assert replaced is not config
+    assert type(replaced) is type(config)
+    assert getattr(replaced, changed_field) == expected_value
+    assert getattr(config, changed_field) == original_changed_value  # original untouched
+    if preserved_field is not None:
+        assert getattr(replaced, preserved_field) == preserved_value
+
+
+# ---------------------------------------------------------------------------
+# ValidationError → InvalidConfigError wrapping (Task 4)
+# ---------------------------------------------------------------------------
+
+
+def test_base_checks_storage_config_wrong_type_raises_invalid_config_error() -> None:
+    """Constructing a BaseChecksStorageConfig subclass with a wrong-typed field must raise
+    InvalidConfigError, not the raw pydantic_core.ValidationError.  DQX callers should not
+    need to import pydantic to handle construction errors from config models.
+    """
+
+    class _StrictConfig(BaseChecksStorageConfig):
+        location: str = "test"
+        count: int  # int field — "not_a_number" cannot be coerced
+
+    with pytest.raises(InvalidConfigError):
+        _StrictConfig(location="test", count="not_a_number")  # type: ignore[arg-type]
diff --git a/tests/unit/test_engine.py b/tests/unit/test_engine.py
index 2ea12d5a3..bb3e409fd 100644
--- a/tests/unit/test_engine.py
+++ b/tests/unit/test_engine.py
@@ -23,8 +23,9 @@
 from databricks.labs.dqx.engine import DQEngine, DQEngineCore
 from databricks.labs.dqx.engine import InvalidCheckError, InvalidParameterError
 from databricks.labs.dqx.metrics_observer import DQMetricsObserver
+from databricks.labs.dqx.rule import DQDatasetRule, CHECK_FUNC_REGISTRY_ORIGINAL_COLUMNS_PRESELECTION
 from databricks.labs.dqx.check_funcs import make_condition
-from databricks.labs.dqx.rule import DQDatasetRule, DQRowRule, register_rule, requires_dbr_version
+from databricks.labs.dqx.rule import DQRowRule, register_rule, requires_dbr_version
 
 
 def test_engine_creation():
@@ -368,6 +369,80 @@ def test_apply_checks_by_metadata_and_save_in_table_raises_when_no_destination_c
         )
 
 
+# ---------------------------------------------------------------------------
+# Regression: _preselect_original_columns uses DQRule.replace() correctly
+# ---------------------------------------------------------------------------
+
+
+def _preselect_dataset_check():
+    """Minimal dataset check accepted by _preselect_original_columns tests."""
+    return F.lit(True), lambda df, col_name: df.withColumn(col_name, F.lit(True))
+
+
+def test_preselect_original_columns_no_op_when_func_not_registered():
+    """_preselect_original_columns returns the same rule object when the check function
+    is not in CHECK_FUNC_REGISTRY_ORIGINAL_COLUMNS_PRESELECTION."""
+    spark_mock = create_autospec(SparkSession)
+    ws = create_autospec(WorkspaceClient)
+    engine = DQEngineCore(spark=spark_mock, workspace_client=ws)
+
+    rule = DQDatasetRule(check_func=_preselect_dataset_check)
+    assert _preselect_dataset_check.__name__ not in CHECK_FUNC_REGISTRY_ORIGINAL_COLUMNS_PRESELECTION
+
+    df = Mock()
+    df.columns = ["id", "name", "_errors", "_warnings"]
+    result = engine._preselect_original_columns(df, rule)
+
+    assert result is rule  # no change — same object returned
+
+
+def test_preselect_original_columns_injects_df_columns(monkeypatch):
+    """_preselect_original_columns must inject df columns (minus result columns) into
+    check_func_kwargs["columns"] via DQRule.replace() when the check function is registered
+    for original-columns preselection and no columns are already provided."""
+    spark_mock = create_autospec(SparkSession)
+    ws = create_autospec(WorkspaceClient)
+    engine = DQEngineCore(spark=spark_mock, workspace_client=ws)
+
+    monkeypatch.setattr(
+        engine_module,
+        "CHECK_FUNC_REGISTRY_ORIGINAL_COLUMNS_PRESELECTION",
+        {_preselect_dataset_check.__name__},
+    )
+
+    rule = DQDatasetRule(check_func=_preselect_dataset_check)
+
+    df = Mock()
+    df.columns = ["id", "name", "_errors", "_warnings"]
+
+    result = engine._preselect_original_columns(df, rule)
+
+    assert result is not rule  # new object produced by replace()
+    assert result.check_func_kwargs["columns"] == ["id", "name"]
+
+
+def test_preselect_original_columns_no_op_when_columns_already_in_kwargs(monkeypatch):
+    """_preselect_original_columns must not overwrite columns already in check_func_kwargs."""
+    spark_mock = create_autospec(SparkSession)
+    ws = create_autospec(WorkspaceClient)
+    engine = DQEngineCore(spark=spark_mock, workspace_client=ws)
+
+    monkeypatch.setattr(
+        engine_module,
+        "CHECK_FUNC_REGISTRY_ORIGINAL_COLUMNS_PRESELECTION",
+        {_preselect_dataset_check.__name__},
+    )
+
+    rule = DQDatasetRule(check_func=_preselect_dataset_check, check_func_kwargs={"columns": ["id"]})
+
+    df = Mock()
+    df.columns = ["id", "name", "_errors", "_warnings"]
+
+    result = engine._preselect_original_columns(df, rule)
+
+    assert result is rule  # early-return — columns already provided
+
+
 # --- DBR version validation error path tests ---
 
 
diff --git a/tests/unit/test_lakebase_config.py b/tests/unit/test_lakebase_config.py
index 8dfec58d2..6533b4c9a 100644
--- a/tests/unit/test_lakebase_config.py
+++ b/tests/unit/test_lakebase_config.py
@@ -286,3 +286,38 @@ def test_lakebase_config_valid_modes():
         mode="overwrite",
     )
     assert config_overwrite.mode == "overwrite"
+
+
+def test_replace_returns_new_validated_instance_and_preserves_other_fields():
+    """`replace()` returns a new config of the same type with overrides applied, other fields
+    preserved, and the original (frozen) instance untouched."""
+    config = LakebaseChecksStorageConfig(
+        location="db.schema.table",
+        instance_name="instance",
+        client_id="00000000-0000-0000-0000-000000000000",
+        mode="append",
+    )
+
+    replaced = config.replace(mode="overwrite")
+
+    assert replaced is not config
+    assert isinstance(replaced, LakebaseChecksStorageConfig)
+    assert replaced.mode == "overwrite"  # overridden
+    assert replaced.location == "db.schema.table"  # preserved
+    assert replaced.instance_name == "instance"  # preserved
+    assert config.mode == "append"  # original untouched
+
+
+def test_replace_reruns_validation():
+    """`replace()` rebuilds through the constructor, so validators re-run against the new fields.
+
+    Unlike `model_copy(update=...)`, which shallow-copies and skips validation, an invalid override
+    must be rejected at the point of the change rather than silently producing an invalid config."""
+    config = LakebaseChecksStorageConfig(
+        location="db.schema.table",
+        instance_name="instance",
+        client_id="00000000-0000-0000-0000-000000000000",
+    )
+
+    with pytest.raises(InvalidConfigError, match="Invalid mode 'BOGUS'. Must be 'append' or 'overwrite'."):
+        config.replace(mode="BOGUS")
diff --git a/tests/unit/test_rule.py b/tests/unit/test_rule.py
index 3b683ec92..64b8c3d03 100644
--- a/tests/unit/test_rule.py
+++ b/tests/unit/test_rule.py
@@ -1,8 +1,11 @@
-"""Unit tests for rule fingerprinting, expansion, and serialization alignment."""
+"""Unit tests for rule fingerprinting, replacement, expansion, and serialization alignment."""
 
 import re
 
-from databricks.labs.dqx.check_funcs import is_not_null, is_not_null_and_not_empty
+import pytest
+
+from databricks.labs.dqx.check_funcs import is_not_null, is_not_null_and_not_empty, is_unique
+from databricks.labs.dqx.errors import InvalidCheckError
 from databricks.labs.dqx.rule import DQRowRule
 from databricks.labs.dqx.rule_fingerprint import (
     compute_rule_fingerprint,
@@ -282,3 +285,49 @@ def test_compute_rule_fingerprint_none_arguments_same_as_empty():
     fp_none = compute_rule_fingerprint(check_none_args)
     fp_empty = compute_rule_fingerprint(check_empty_args)
     assert fp_none == fp_empty
+
+
+def test_dq_rule_replace_returns_new_instance_with_overrides_and_preserves_other_fields():
+    """`replace()` returns a new rule of the same concrete type with overrides applied and other
+    fields preserved, leaving the original (frozen) instance untouched."""
+    rule = DQRowRule(name="id_not_null", criticality="error", check_func=is_not_null, column="id")
+
+    replaced = rule.replace(criticality="warn")
+
+    assert replaced is not rule
+    assert isinstance(replaced, DQRowRule)
+    assert replaced.criticality == "warn"  # overridden
+    assert replaced.column == "id"  # preserved
+    assert replaced.check_func is is_not_null  # preserved
+    assert replaced.name == "id_not_null"  # preserved
+    assert rule.criticality == "error"  # original is frozen and must be untouched
+
+
+def test_dq_rule_replace_recomputes_cached_fingerprint_from_updated_fields():
+    """`replace()` recomputes `functools.cached_property` state instead of copying it stale.
+
+    `rule_fingerprint` is cached and derived from the rule's fields. After the cache is populated,
+    replacing a fingerprint-affecting field must yield the fingerprint of an equivalently-built rule,
+    not the stale pre-replace value. A shallow `model_copy(update=...)` would carry over the cached
+    hash and fail this assertion."""
+    # `name` is part of the fingerprint, so it is held constant across rule/replaced/expected to
+    # isolate `column` as the only fingerprint-affecting difference.
+    rule = DQRowRule(name="shared", criticality="error", check_func=is_not_null, column="id")
+    original_fingerprint = rule.rule_fingerprint  # populate the cached_property before replacing
+
+    replaced = rule.replace(column="other_col")
+
+    expected = DQRowRule(name="shared", criticality="error", check_func=is_not_null, column="other_col")
+    assert replaced.rule_fingerprint == expected.rule_fingerprint
+    assert replaced.rule_fingerprint != original_fingerprint
+
+
+def test_dq_rule_replace_reruns_validation():
+    """`replace()` rebuilds through the constructor, so validators re-run against the new fields.
+
+    Swapping a dataset-level function onto a row-level rule must raise `InvalidCheckError`; a shallow
+    `model_copy(update=...)` would skip validation and silently produce an invalid rule."""
+    rule = DQRowRule(name="id_not_null", criticality="error", check_func=is_not_null, column="id")
+
+    with pytest.raises(InvalidCheckError):
+        rule.replace(check_func=is_unique)
diff --git a/uv.lock b/uv.lock
index 32801535f..fd915e335 100644
--- a/uv.lock
+++ b/uv.lock
@@ -31,18 +31,18 @@ dependencies = [
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", upload-time = "2025-03-12T01:42:47.083Z" },
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