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ExpertHalf day to full dayInstructor visible
Data Sciences Expert capstone
A team needs a production-ready data sciences design with evidence that it survives failure and rollback.
Business context
Ultiblob uses this exercise to train data analyst, data scientist, and analytics engineer candidates on realistic private-cloud lab operations rather than static videos.
Technical objective
Design, validate, document, and recover a data sciences environment using the provided templates and checks.
Student instructions
- 1Open the lab workspace and review the topology map.
- 2Launch the required templates and wait for all provisioning checks to complete.
- 3Complete the configuration task in the course module.
- 4Run validation and capture the result for instructor review.
- 5Create a snapshot before any risky troubleshooting or failure exercise.
Troubleshooting
- If access fails, confirm the bastion session is active and the instance is not expired.
- If validation fails, inspect the lab event log before rerunning the check.
- If configuration drifts, restore the latest clean snapshot and repeat the module task.
Cleanup
- Export notes or reports required by the instructor.
- Restore or delete temporary snapshots created during the exercise.
- Use the teardown action when the module is complete or allow the TTL policy to expire the lab.
Launch flow
Provisioning readiness
Waiting for launch
Click Launch lab to start the provisioning flow and watch each stage complete.
0%
- Request accepted
- Capacity reserved
- Templates queued
- Validation running
- Workspace ready
notebook-reachable
Pendingvm-reachable
PendingRequired templates
- Python data science workstation - defined
- JupyterLab server - defined
- PostgreSQL database - defined
Validation checks
- Notebook reachable: JupyterLab returns a valid login page or authenticated health response.
- VM reachable: The VM reports boot complete and responds through the tenant bastion path.
Expected result
The lab reaches Healthy state for Notebook reachable, VM reachable.
Reset policy: Student can reset to the last clean snapshot; instructor can force reset from admin view. Teardown policy: Automatic teardown at TTL expiry with manual instructor override for cohorts.