Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer manages a Databricks SQL warehouse that serves a dashboard used by the finance team. The dashboard queries have become slow during peak hours, and the engineer suspects that some queries are scanning excessive data. Which system table should the engineer query to analyze query performance and identify expensive queries?
⚠ Common exam trap
Watch out — candidates often confuse system tables that track access or billing with those that track query performance, leading to selection of an audit or billing table instead of query history.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
system.query.history
The system.query.history table is designed for query observability in Databricks SQL. It captures execution details, including query duration, rows produced, and bytes read, enabling engineers to pinpoint inefficient queries. Other system tables focus on compute metrics, audit events, or billing, and lack the query-level performance data needed to troubleshoot slow dashboards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
system.billing.usage
Why it's wrong here
system.billing.usage tracks billing and usage data, such as DBUs consumed by clusters and SQL warehouses. It can help with cost analysis but does not provide query-level performance details. It cannot reveal which specific queries are slow or scanning excessive data, so it is not the right choice here.
- ✗
system.access.audit
Why it's wrong here
system.access.audit records audit events such as who accessed what resource, login activity, and permission changes. While it can show that a query was executed, it does not include performance metrics like execution time or data scanned. Therefore, it is not suitable for identifying slow or expensive queries in this scenario.
- ✗
system.compute.node_timeline
Why it's wrong here
The system.compute.node_timeline table provides historical node-level metrics such as CPU and memory usage for compute resources, not query-level performance details. It is useful for cluster health monitoring but does not contain query execution statistics like duration, rows read, or query text, so it cannot identify expensive SQL queries in this scenario.
- ✓
system.query.history
Why this is correct
system.query.history contains detailed records of query executions, including query text, duration, rows read, bytes scanned, and user information. By querying this table, the engineer can identify long-running or high-scan queries that impact dashboard performance. This is the correct system table for analyzing query performance in Databricks SQL.
About these practice questions
Courseiva writes every Databricks-DE-Pro question from scratch — 267 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.