Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which TWO factors influence the effective parallelism of a Spark application?
⚠ Common exam trap
Candidates often focus only on the number of partitions. They fail to realize that having many partitions is useless if there are insufficient CPU cores to process them in parallel.
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
✓
The number of RDD or DataFrame partitions.
Effective parallelism in Spark is governed by the number of partitions created in the data and the number of available cores in the executor pool. If the partition count is too low, the cluster is underutilized. If the core count is too low, tasks are queued, creating bottlenecks. Managing these two factors is the primary way to ensure that the workload is spread evenly across the available hardware resources for maximum throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The number of RDD or DataFrame partitions.
Why this is correct
Partitions are the fundamental unit of parallelism in Spark. Each partition corresponds to one task. Increasing the number of partitions allows for more concurrent tasks, assuming there is sufficient CPU capacity on the executors to process them simultaneously, directly affecting the job's execution speed.
- ✗
The total amount of disk space on the worker nodes.
Why it's wrong here
While disk space is necessary for overflow or shuffle files, it is not a direct factor in determining the degree of parallelism. Parallelism is defined by compute resources, not storage capacity; having large disks does not allow more tasks to run in parallel on an executor.
- ✓
The number of available CPU cores in the executor pool.
Why this is correct
The number of cores defines how many tasks can be processed at the same time across the cluster. Even with many partitions, if the core count is low, tasks will have to wait in a queue, limiting the effective parallelism of the application's execution.
- ✗
The network latency between the driver and the cluster manager.
Why it's wrong here
Network latency between the driver and the cluster manager impacts the time taken for negotiation and initial startup, but it does not dictate the parallelism of the tasks themselves once they are running on the executors, which is determined by data and compute limits.
- ✗
The size of the broadcast variables.
Why it's wrong here
Broadcast variables are used to distribute read-only data efficiently to all nodes. While their size affects memory usage and network overhead during the broadcast phase, they do not inherently limit or dictate the degree of task parallelism within the cluster for the job's execution.
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JA
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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