Monitoring and Alerting — Databricks Certified Data Engineer Professional

The official DATABRICKS documentation our Monitoring and Alerting practice questions are cited to. Review the primary sources, then practise.

Official references for this objective

Databricks — Beyond Monitoring: Lakeflow Observability for Operational Health | Databricks

how Databricks system tables provide the historical depth needed for root-cause analysis, performance analysis, cost tracking, and reliability reporting at scale
Databricks — AI_DIAGNOSE: Automating Spark Job Debugging With LLM Agents | Databricks

how the agent autonomously gathers context, retrieves distributed logs, and analyzes job failures
Databricks — Databricks FinOps Genie — Cost Observability Meets Optimization Insights | Databricks

a cost observability and optimization capability that combines a Databricks‑native FinOps dashboard, Genie space and FinOps agent, proven at scale across 1,000+ workspaces
Databricks — Inside Adobe’s Near Real-Time Cloud Spend Monitoring System | Databricks

We solved this by combining Databricks system tables—`billing.usage`, `query.history`, and `compute.warehouse_events`—to enable query-level cost attribution without added compute.
Databricks — Operating Databricks at Enterprise Scale: 60–70% MTTR Reduction Through SQL & AI Functions | Databricks

Databricks SQL and AI functions create an autonomous optimization engine, Unity Catalog ensures enterprise governance and production-safe validation guarantees zero SLA risk