A daily revenue pipeline has no orchestrator-level SLA; the freshness commitment lives only in a run
A medium Pipeline Design mock interview question on DataDriven. Practice with AI-powered feedback, real code execution, and a hire/no-hire decision.
- Domain
- Pipeline Design
- Difficulty
- medium
Interview Prompt
A daily revenue pipeline has no orchestrator-level SLA; the freshness commitment lives only in a runbook. When the pipeline misses 6am, the on-call notices late. Apply the SLA-at-seam framing this section just taught and declare the SLA structurally. Add an observability_tool node (Monte Carlo, Bigeye, Anomalo, or Datadog) that monitors mart.daily_revenue's freshness, and an alert_destination node (PagerDuty, OpsGenie, Slack, email, or SES) that routes SLA misses to on-call. The observability_tool defines the SLA threshold; the alert_destination is where misses are paged. Together they make the SLA a live, monitored commitment instead of a runbook entry.
How This Interview Works
- Read the vague prompt (just like a real interview)
- Ask clarifying questions to the AI interviewer
- Write your pipeline design solution with real code execution
- Get instant feedback and a hire/no-hire decision