Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
A data analyst is running a complex, resource-intensive query inside the Databricks SQL query editor that frequently times out before returning results. Which administrative feature should the analyst or workspace admin leverage to manage compute resources and prevent long-running queries from blocking other users?
⚠ Common exam trap
Candidates often suggest increasing the warehouse size (scaling up). While this might help, the administrative best practice for runaway queries is setting explicit timeouts to protect shared resources.
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
✓
Configure the automated query timeout setting on the SQL warehouse to automatically abort queries exceeding a specific duration.
Databricks SQL warehouses support query timeout limits and auto-stopping features that prevent runaway queries from consuming cluster resources indefinitely. Configuring these timeouts ensures cluster availability and protects shared analytical environments from deadlocks and excessive consumption, making it a critical skill for efficient query execution management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum number of concurrent users in the SQL warehouse cluster settings.
Why it's wrong here
Raising the concurrent-user limit only admits more simultaneous queries onto the same warehouse; it does not cap query runtime or isolate one user's long-running statement, so the timeout and blocking persist. It would be correct when many short queries queue behind the connection limit rather than one heavy query monopolising compute.
- ✓
Configure the automated query timeout setting on the SQL warehouse to automatically abort queries exceeding a specific duration.
Why this is correct
The SQL warehouse automated query timeout setting aborts statements exceeding a defined duration, freeing compute so long-running queries cannot monopolise resources and block other users. This directly addresses the timeout symptom while enforcing resource governance at the warehouse level.
- ✗
Convert the query into a Spark Structured Streaming job using the Databricks notebook interface.
Why it's wrong here
Structured Streaming processes continuous incremental data arriving from sources such as Kafka or Auto Loader; it cannot execute an ad-hoc analytical query against Delta tables, so it neither returns the result nor prevents warehouse contention. It would be correct when the requirement is continuous ingestion with incremental processing rather than a one-off interactive query.
- ✗
Manually restart the underlying Apache Spark driver node using the cluster UI restart button.
Why it's wrong here
Restarting the driver terminates all running statements and cached state on that cluster, returning no results and disrupting every other user; it addresses neither query duration nor resource governance. It would be correct for recovering a cluster stuck through driver failure or unresponsive Spark sessions, not for managing long-running queries.
About these practice questions
This Databricks-DA-Assoc question is part of Courseiva's 291-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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-DA-Assoc 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-DA-Assoc exam.