Section 7: Lab: Production-Grade Error Handling
- On block error and per-action On error settings such as retry and Go to label
- Get last error and taking a screenshot when an error occurs
- Design flows that move on to the next item when one item fails
- Lab: a flow that skips bad records, logs the reason and finishes the run
Section 8: Lab: Logging, Debugging and Testing
- Write run logs with timestamps and status to a file or a central table
- Breakpoints, Run from here and inspecting variables during a run
- Test with many cases, including badly formatted data
- Lab: add logging and an end-of-run summary to an existing flow
Section 9: Handling Secrets and Credentials Securely
- Sensitive variables and sensitive input variables that stay out of logs
- Never hard-code passwords, and an overview of credentials backed by Azure Key Vault
- Permissions for the account that runs the flow, and least privilege
- Lab: move passwords out of a flow into sensitive inputs
Section 10: Lab: Triggering Desktop Flows from Cloud Flows
- Register a machine and create a desktop flow connection
- Pass input variables from a cloud flow and use the outputs that come back
- Attended versus unattended, and which work suits each
- Lab: a cloud flow that picks up a file from email and has a desktop flow process it
Section 11: Machine Groups, Unattended Runs and Monitoring
- Machine groups, run queues and priority when several jobs arrive at once
- Unattended requirements such as Windows sessions and screen resolution
- Monitor desktop flow runs and alert when a run fails
- Licences for attended and unattended runs, and an overview of hosted machines
Section 12: Workshop: End-to-End RPA Capstone
- Pick real work that re-keys data between systems, such as invoices, purchase orders or daily reports
- Build a desktop flow with subflows, robust selectors, error handling and logging
- Trigger it from a cloud flow and report results through Teams or email
- Present the work, review it together and wrap up with a pre-production checklist