Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which Spark configuration property determines the maximum amount of memory the Spark Driver can request for itself when running on a Kubernetes cluster?
⚠ Common exam trap
Candidates often confuse 'spark.driver.memory' with 'spark.executor.memory', assuming the same property applies to both the driver and the worker processes.
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.driver.memory
The 'spark.driver.memory' property is the primary configuration used to define the heap size for the Spark Driver process. In Kubernetes environments, this value is translated into the container resource requests for the driver pod. Proper sizing prevents OOM errors during large collect operations or when handling massive metadata for complex query plans, ensuring the driver maintains stability throughout the application lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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spark.executor.memory
Why it's wrong here
This property controls the memory allocation for each executor process, not the driver. It determines the size of the heap available for tasks and storage on the worker nodes, meaning it has no direct impact on the memory limit imposed on the driver pod in Kubernetes.
- ✓
spark.driver.memory
Why this is correct
This property explicitly sets the heap size for the Spark Driver process. In a containerized environment like Kubernetes, the cluster manager uses this value to allocate resources for the driver pod, ensuring the driver has sufficient memory to manage the job's metadata and DAG scheduling requirements.
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spark.memory.fraction
Why it's wrong here
This is a tuning parameter that controls the ratio of heap space allocated to execution versus storage. It operates within the memory already allocated to a process, rather than determining the total memory size allocated to the driver or executor process from the underlying infrastructure.
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spark.kubernetes.driver.limit.memory
Why it's wrong here
While this property exists to set a hard limit on the driver container, it is not the primary configuration for the Spark application heap size. Relying solely on this without setting the JVM heap size can lead to inefficient memory usage and potential container restarts by the orchestrator.
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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
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