Databricks-DE-Pro Debugging and Deploying Practice Question
Exhibit
CLI Output: databricks jobs run-now --job-id 5678
{ "error": "INVALID_PARAMETER_VALUE", "message": "The job 5678 is currently running and does not allow concurrent runs." }Refer to the exhibit. Which configuration change is required to enable multiple instances of this job to run simultaneously?
⚠ Common exam trap
Candidates often look for code-level synchronization locks or cluster scaling options, missing that Databricks job settings explicitly block concurrent runs by default.
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
✓
Set max_concurrent_runs to a value greater than 1 in the job settings.
The error message explicitly states that the job does not allow concurrent runs. By default, many jobs are configured for single-instance execution to prevent data contention or race conditions. To allow overlapping executions, the 'max_concurrent_runs' parameter must be adjusted in the job settings. This is a critical configuration for scenarios where independent data partitions need to be processed in parallel to meet aggressive latency requirements in a production system.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the timeout threshold for the job.
Why it's wrong here
The timeout setting defines how long a job can run before it is automatically cancelled. It does not control concurrency. Modifying the timeout will not allow a second instance of the job to start while the first is already running; it only changes the termination criteria.
- ✓
Set max_concurrent_runs to a value greater than 1 in the job settings.
Why this is correct
The max_concurrent_runs parameter controls how many instances of a job can execute at the same time. Setting this to a value higher than 1 allows the job to be triggered even if a previous run is still in progress, enabling parallel processing of independent data workflows.
- ✗
Add more workers to the cluster assigned to the job.
Why it's wrong here
Scaling out the cluster increases the compute resources available to the job, but it does not change the concurrency policy defined for the job itself. Even with a massive cluster, the job remains restricted by the concurrency limit, which is a logic control, not a resource control.
- ✗
Enable 'Retries' in the job configuration.
Why it's wrong here
Retries are intended for handling transient failures by automatically restarting a failed task or job. They are unrelated to the policy governing concurrent execution. Enabling retries will not permit multiple simultaneous runs of the same job identifier, as that is a separate configuration parameter designed for managing execution flow.
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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
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