Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
A developer is troubleshooting a Spark job that fails with an OutOfMemoryError on the driver. The job collects a large DataFrame to the driver using .collect() and then processes it locally. The developer wants to avoid the driver OOM while still obtaining the results. Which approach is most appropriate?
⚠ Common exam trap
The trap here is believing that increasing driver memory is a sustainable fix, when the real solution is to avoid collecting large data to the driver altogether.
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
✓
Write the DataFrame to a distributed storage system and then read it back in smaller chunks for local processing.
Collecting a large DataFrame to the driver is an anti-pattern because it moves all data to a single node, risking OOM. The scalable solution is to persist the DataFrame to distributed storage and then read it in smaller partitions or batches for local processing. This maintains distribution and avoids overwhelming the driver.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace .collect() with .take(1000) to limit the number of rows returned.
Why it's wrong here
Using .take(1000) reduces the data transferred to the driver, but it only returns the first 1000 rows. If the developer needs the full result set, this approach is insufficient. It might avoid OOM but does not solve the underlying need to process all data, and the driver could still OOM if the subset is large. It is a partial workaround, not a general solution.
- ✗
Increase spark.driver.memory to a very large value to accommodate the collected data.
Why it's wrong here
Increasing driver memory might temporarily allow the collection to succeed, but it is not scalable. The driver is a single point of failure and has limited resources. If the dataset grows, the driver will eventually OOM again. This approach also ignores best practices of keeping large data distributed and only collecting aggregated or summary results.
- ✗
Use .foreach() to process each row on the driver instead of collecting the entire DataFrame.
Why it's wrong here
.foreach() on a DataFrame executes the provided function on the executors, not the driver, for each row. To process data locally on the driver, one would need to collect it first, which defeats the purpose. Using .foreach() does not bring data to the driver, so it cannot be used for local processing. It is also not available on DataFrames in PySpark in the same way as on RDDs.
- ✓
Write the DataFrame to a distributed storage system and then read it back in smaller chunks for local processing.
Why this is correct
Writing the DataFrame to distributed storage (e.g., Delta Lake, Parquet) and then reading it in batches avoids bringing the entire dataset to the driver at once. This leverages Spark's distributed nature for the heavy lifting and allows the driver to process manageable chunks. It is a scalable pattern that prevents driver OOM while still enabling access to all data.
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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-Spark-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-Spark-Assoc exam.