Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A developer is debugging a Spark job and observes that a particular stage has 200 tasks, but only 10 executors with 2 cores each are available. What will happen to the remaining tasks in that stage?
⚠ Common exam trap
The trap here is thinking that Spark dynamically adjusts partition count to fit available cores, when in reality it queues tasks and runs them in waves based on core availability.
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 tasks will be queued and executed as cores become available, up to 20 concurrently
The number of concurrently running tasks is bounded by the total number of cores across executors. Here, 10 executors with 2 cores each provide 20 slots, so 20 tasks run at a time while the remaining 180 wait in the scheduler's queue. As tasks finish, new ones are launched. Spark does not fail or coalesce tasks due to resource scarcity.
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 tasks will fail with an insufficient resources error
Why it's wrong here
Spark does not fail tasks simply because there are more tasks than available cores; it queues them. Insufficient resources errors occur when the cluster cannot allocate any executors, not when tasks outnumber slots. The Task Scheduler handles the backlog gracefully, so this outcome is incorrect for the scenario.
- ✗
The tasks will be executed on the driver node to compensate for the lack of executors
Why it's wrong here
The driver does not execute tasks; it only schedules them and manages the application. Executors are responsible for running tasks. In some local modes the driver can act as an executor, but in a cluster deployment like Databricks, the driver is separate. Thus, tasks are not offloaded to the driver to compensate for limited executors.
- ✓
The tasks will be queued and executed as cores become available, up to 20 concurrently
Why this is correct
With 10 executors each having 2 cores, the cluster can run 20 tasks concurrently. The Task Scheduler will launch tasks as slots free up, so the 200 tasks are processed in waves. This is standard behavior: the number of concurrent tasks is limited by total cores, and the rest wait in the queue until resources are available.
- ✗
The tasks will be automatically coalesced to match the number of available cores
Why it's wrong here
Spark does not dynamically reduce the number of tasks in a stage to match available cores. The partition count is determined by the RDD lineage and transformations, not by cluster resources. Coalescing only happens if explicitly called. Therefore, the tasks remain 200 and are scheduled in waves, not merged.
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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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