Updated 404 link, broken link and link in doc (#5800)

website/docs/docs/build/python-models.md

@@ -86,7 +86,7 @@ Each Python model lives in a `.py` file in your `models/` folder. It defines a f
 - **`dbt`**: A class compiled by dbt Core, unique to each model, enables you to run your Python code in the context of your dbt project and DAG.
 - **`session`**: A class representing your data platform’s connection to the Python backend. The session is needed to read in tables as DataFrames, and to write DataFrames back to tables. In PySpark, by convention, the `SparkSession` is named `spark`, and available globally. For consistency across platforms, we always pass it into the `model` function as an explicit argument called `session`.
 
-The `model()` function must return a single DataFrame. On Snowpark (Snowflake), this can be a Snowpark or pandas DataFrame. Via PySpark (Databricks + BigQuery), this can be a Spark, pandas, or pandas-on-Spark DataFrame. For more about choosing between pandas and native DataFrames, see [DataFrame API + syntax](#dataframe-api--syntax).
+The `model()` function must return a single DataFrame. On Snowpark (Snowflake), this can be a Snowpark or pandas DataFrame. Via PySpark (Databricks + BigQuery), this can be a Spark, pandas, or pandas-on-Spark DataFrame. For more about choosing between pandas and native DataFrames, see [DataFrame API + syntax](#dataframe-api-and-syntax).
 
 When you `dbt run --select python_model`, dbt will prepare and pass in both arguments (`dbt` and `session`). All you have to do is define the function. This is how every single Python model should look:
 
@@ -513,7 +513,7 @@ def model(dbt, session):
 
 **Note:** Due to a Snowpark limitation, it is not currently possible to register complex named UDFs within stored procedures and, therefore, dbt Python models. We are looking to add native support for Python UDFs as a project/DAG resource type in a future release. For the time being, if you want to create a "vectorized" Python UDF via the Batch API, we recommend either:
 - Writing [`create function`](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-batch.html) inside a SQL macro, to run as a hook or run-operation
-- [Registering from a staged file](https://docs.snowflake.com/ko/developer-guide/snowpark/reference/python/_autosummary/snowflake.snowpark.udf.html#snowflake.snowpark.udf.UDFRegistration.register_from_file) within your Python model code
+- [Registering from a staged file](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs#creating-a-udf-from-a-python-source-file) within your Python model code
 
 </div>
 
@@ -610,7 +610,7 @@ In their initial launch, Python models are supported on three of the most popula
 
 **Additional setup:** You will need to [acknowledge and accept Snowflake Third Party Terms](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#getting-started) to use Anaconda packages.
 
-**Installing packages:** Snowpark supports several popular packages via Anaconda. The complete list is at https://repo.anaconda.com/pkgs/snowflake/. Packages are installed at the time your model is being run. Different models can have different package dependencies. If you are using third-party packages, Snowflake recommends using a dedicated virtual warehouse for best performance rather than one with many concurrent users.
+**Installing packages:** Snowpark supports several popular packages via Anaconda. Refer to the [complete list](https://repo.anaconda.com/pkgs/snowflake/) for more details. Packages are installed when your model is run. Different models can have different package dependencies. If you use third-party packages, Snowflake recommends using a dedicated virtual warehouse for best performance rather than one with many concurrent users.
 
 **Python version:** To specify a different python version, use the following configuration:
 ```