Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst runs a long-running query that fails with an 'Out of Memory' (OOM) error. Which approach is the most effective way to resolve this without changing the underlying data structure?
⚠ Common exam trap
Candidates mistakenly suggest changing the data storage format or reducing the number of rows, which are inefficient compared to simply scaling the compute resources for the query.
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
✓
Resize the SQL Warehouse to a larger instance type
OOM errors in Databricks SQL often occur due to large joins or aggregations that exceed available executor memory. Using hints, such as a broadcast join hint, can instruct the engine to handle data movement differently. If a specific join is causing the OOM, forcing a broadcast join (for small tables) or increasing the memory of the SQL warehouse can provide the necessary resources to complete the task.
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 number of partitions to 10,000
Why it's wrong here
Increasing the number of partitions to an excessively high value will likely exacerbate memory pressure rather than resolve it. It creates a massive number of tasks, increasing the metadata management load and memory overhead for the task scheduler, which often leads to further instability and OOM errors.
- ✗
Use a broadcast hint for large-to-large table joins
Why it's wrong here
Broadcasting requires the entire table to fit in the memory of every node in the cluster. For large-to-large joins, this will immediately trigger an OOM error because the table size will exceed available memory. Broadcast hints should only be used when one side of the join is small.
- ✓
Resize the SQL Warehouse to a larger instance type
Why this is correct
Resizing the SQL Warehouse to a larger instance type provides more RAM per node, which is the most direct solution for OOM errors occurring during memory-intensive operations like large joins or complex aggregations. It allows the engine to handle larger intermediate datasets without needing to spill to disk.
- ✗
Reduce the number of columns selected in the query
Why it's wrong here
While reducing the number of columns is a best practice for I/O efficiency, it rarely solves an OOM error caused by large-scale joins or aggregations. The memory bottleneck usually stems from the volume of data being joined or aggregated, which persists regardless of the column count.
Visual reference
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.