Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A Databricks job is failing due to 'Out of Memory' (OOM) errors during a join operation on two large datasets. Which TWO actions could help mitigate this issue?
⚠ Common exam trap
Candidates often try to optimize joins by enabling broadcast thresholds on massive datasets, accidentally triggering Out of Memory errors instead of disabling the threshold.
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
✓
Increase the cluster's worker node instance type with more memory.
OOM errors during joins often occur because the driver or worker nodes lack enough memory to handle the shuffle or broadcast operations. Increasing the cluster memory allows for larger data partitions to reside in memory, while adjusting the broadcast join threshold prevents the optimizer from attempting to broadcast datasets that exceed available node capacity. These configurations are essential for stabilizing large-scale ETL pipelines that process high-volume data.
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 cluster's worker node instance type with more memory.
Why this is correct
Scaling up to worker nodes with higher memory capacity provides the Spark executors with more heap space. This allows them to process larger partitions and handle complex shuffle operations without spilling to disk or triggering OOM exceptions. This is the most direct hardware-level fix for memory-intensive join operations.
- ✗
Decrease the number of partitions in the Spark cluster.
Why it's wrong here
Decreasing the number of partitions results in larger individual partitions being processed by each executor. This actually increases the likelihood of OOM errors because each executor must manage a larger chunk of data at once. Effective Spark tuning typically involves increasing partition counts to distribute memory pressure across nodes.
- ✓
Disable the spark.sql.autoBroadcastJoinThreshold configuration.
Why this is correct
Broadcasting an excessively large table triggers OOM errors if the table exceeds the executor memory limit. By setting this threshold to a lower value or disabling it, you force Spark to perform a sort-merge join instead of a broadcast join, which is more robust for large datasets that do not fit in memory.
- ✗
Enable dynamic allocation for the cluster.
Why it's wrong here
Dynamic allocation adjusts the number of executors based on the current workload. While it helps with resource efficiency and cost, it does not increase the per-executor memory available for a specific task. If a single task is too large for an executor, dynamic allocation will not prevent the OOM error.
- ✗
Use the cache() method on every DataFrame in the pipeline.
Why it's wrong here
Caching all DataFrames indiscriminately consumes excessive memory and often leads to even faster OOM failures. Caching should be used strategically only on data that is reused multiple times within the same job, and even then, it should be managed carefully to ensure it does not exhaust the available heap.
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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.