Upload of MLTable in automl-forecasting-task-energy-demand-advanced from the azuerml-examples not working properly

### Operating System

Windows

### Version Information

Python version: 3.11.1
azure-ai-ml package version: 1.3.0

### Steps to reproduce
Alternatively to the steps below, run the notebook _automl-forecasting-task-energy-demand-advanced_ directly in Azure Machine Learning Studio. Python 3.10 - SDK V2.

1. Clone repo and open the _automl-forecasting-task-energy-demand-advance_ jupyter notebook in VS Code.
2. Update the credential to be used to `credential = InteractiveBrowserCredential(tenant_id=" Encountered user error while fetching data from Dataset. Error: UserErrorException:
> Message: MLTable yaml schema is invalid:
> Error Code: Validation
> Validation Error Code: Invalid MLTable
> Validation Target: MLTableToDataflow
> Error Message: Failed to convert a MLTable to dataflow
> uri path is not a valid datastore uri path
> | session_id=e2cb5fb1-f330-4b5d-a2f2-7784642128f9
> InnerException None
> ErrorResponse
> {
> "error": {
> "code": "UserError",
> "message": "MLTable yaml schema is invalid: \nError Code: Validation\nValidation Error Code: Invalid MLTable\nValidation Target: MLTableToDataflow\nError Message: Failed to convert a MLTable to dataflow\nuri path is not a valid datastore uri path\n| session_id=e2cb5fb1-f330-4b5d-a2f2-7784642128f9"
> }
> }

### Addition information
I've also tried to run the notebook from Azure Machine Learning Studio. This time with Python 3.10 - SDK V2. I get the same error there.
_No response_

Start with the automl-forecasting-task-energy-demand-advanced Jupyter notebook and run its cells using the reported Python and azure-ai-ml versions. Trace the MLTable used by the job and compare its referenced URI with the datastore URI requirement shown in the error. Done means the example job fetches its data and completes without the MLTable validation exception.

Written by the indexing model from the issue text.