Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Exhibit
24/05/10 10:00:00 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources.
Refer to the exhibit. Which configuration issue is most likely causing this warning in a Spark application?
⚠ Common exam trap
Candidates often choose incorrect configuration tweaks like increasing cores or changing shuffle partitions. They miss that the root cause is a resource constraint where the requested memory exceeds physical node capacity.
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 requested spark.executor.memory exceeds node capacity.
This warning indicates the driver is unable to find executors that meet the resource requirements specified for the job. This usually happens when the requested memory or CPU per executor exceeds what is available on the cluster's worker nodes. Checking the resource configuration against the cluster's physical limits is the first step in resolving this scheduling deadlock, as the driver remains idle waiting for resources that will never become available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.driver.memory is set too high.
Why it's wrong here
Driver memory affects the driver node, not the executor resource acquisition process. If the driver cannot start, the application fails entirely, rather than producing a warning in the TaskSchedulerImpl about the inability to accept resources for the initial job, which concerns workers.
- ✓
The requested spark.executor.memory exceeds node capacity.
Why this is correct
When an application requests more memory per executor than a single worker node can provide, the cluster manager cannot allocate the executors. The TaskScheduler will wait indefinitely for these resources, causing this warning as the job remains stuck in a pending state.
- ✗
The shuffle service is not enabled on the cluster.
Why it's wrong here
While the shuffle service is important for performance and stability, its absence does not prevent the initial job from starting or acquiring resources. The application can run without the shuffle service, though it may be prone to failures during shuffle-heavy operations.
- ✗
The number of partitions is set too high for the cluster.
Why it's wrong here
High partition counts may lead to overhead, but they do not prevent the initial job from acquiring resources from the cluster manager. The warning specifically mentions that the job has not 'accepted any resources,' which points toward a resource negotiation issue, not a data processing volume issue.
Visual reference
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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.