Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
Which Spark configuration property can be used to enable Adaptive Query Execution (AQE) in Databricks?
⚠ Common exam trap
Candidates often confuse AQE with general Spark configurations like executor memory or shuffle partitions. They might pick settings that control static partitioning instead of the dynamic runtime optimization framework.
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
✓
spark.sql.adaptive.enabled
Adaptive Query Execution (AQE) is a critical optimization framework in Spark that re-optimizes query plans at runtime based on statistics collected during shuffle stages. It enables features like coalescing shuffle partitions and dynamically switching join strategies. Enabling AQE is a best practice for modern Databricks workloads as it provides significant performance gains without manual intervention, automatically adapting to varying data distributions in production pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
spark.sql.adaptive.enabled
Why this is correct
Setting spark.sql.adaptive.enabled to 'true' is the required configuration to turn on the Adaptive Query Execution framework. Once enabled, Spark will dynamically optimize query execution plans based on runtime statistics, leading to more efficient processing and faster performance for complex SQL queries in Spark 3.x environments.
- ✗
spark.sql.autoBroadcastJoinThreshold
Why it's wrong here
This configuration sets the size threshold for broadcasting tables during a join. While it is an important performance tuning setting, it is not related to enabling the Adaptive Query Execution framework, which handles a much wider range of runtime optimizations beyond just broadcast join decisions.
- ✗
spark.databricks.adaptive.execution
Why it's wrong here
This is not a valid Spark or Databricks configuration property. The correct setting is spark.sql.adaptive.enabled. Using incorrect configuration names will lead to ignored settings and no performance improvements, demonstrating the need for engineers to be familiar with the official Spark documentation for valid optimization parameters.
- ✗
spark.sql.shuffle.partitions
Why it's wrong here
This setting controls the default number of shuffle partitions, which is a static configuration used by Spark before executing a query. It is not the command that enables the AQE engine itself, although AQE can dynamically adjust the number of shuffle partitions if enabled properly.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-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-DE-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-DE-Assoc exam.