Here are some potential causes and solutions:

1. Intermittent Errors: The error being fickle and appearing randomly suggests that it might be related to resource availability or transient issues in the cluster. Ensure that your cluster has sufficient resources allocated, especially since your dataset size ranges from 200k to 1 million rows.

2. Retries Exceeded: can be related to "RETRIES_EXCEEDED" when using the FeatureEngineeringClient. This could be due to network issues, resource constraints, or other transient failures. 

3. Python and PySpark Compatibility: Another snippet highlights issues with Python and PySpark version compatibility. Ensure that the versions of Python and PySpark you are using are compatible with each other and with the Databricks Runtime (DBR) version you are on (15.4 LTS ML).

4. Custom Docker Images: If you are using a custom Docker image, ensure that all dependencies, including PySpark and the databricks-feature-engineering package, are correctly installed and compatible with each other.

5. Code Path Issues: There might be issues related to the code paths or the way modules are imported. Ensure that all necessary modules and paths are correctly set up in your environment.

6. Cluster Configuration: Verify that your cluster configuration (driver and worker nodes) is appropriate for the workload. Sometimes, increasing the number of worker nodes or their size can help resolve intermittent issues.