Azure Machine Learning Execute Pipeline Configuration to pass input data

Question

I would like to create a Synapse Pipeline for batch inferencing with data ingestion to store the data into data lake and then use this as input to call a batch endpoint already created (through ML Execute Pipeline) Then, capture the output into the data lake (appended to a table) to continue the next steps...

The documentation from Microsoft to setup such a scenario is very poor and everything I tried is failing.

Below is the Azure Machine Learning Execute Pipeline configuration. I need to pass the value for the dataset_param with data asset instance already available as below.

But, it complains that the dataset_param is not provided. Not sure, how to pass this value...

Here is the original experiment / pipeline / endpoint created by the DevOps pipeline. I just call this endpoint above from the Synapse Pipeline