Databricks-DE-Assoc Databricks Intelligence Platform Practice Question
Exhibit
{
"cluster_name": "analytics-cluster",
"spark_version": "14.3.x-scala2.12",
"node_type_id": "i3.xlarge",
"autoscale": {
"min_workers": 2,
"max_workers": 8
},
"policy_id": "policy-12345"
}Refer to the exhibit. A data engineer is deploying an automated job. Based on the provided configuration, what is the primary benefit of using the 'autoscale' attribute in this cluster definition?
⚠ Common exam trap
Candidates often mistake autoscaling for spot instance management or automated software patching rather than dynamic resource allocation based on load.
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
✓
It enables the cluster to adjust resources dynamically based on current job load.
The 'autoscale' configuration allows the Databricks Intelligence Platform to dynamically adjust the number of worker nodes based on the workload demands. By setting a minimum and maximum range, the cluster automatically adds nodes during intensive processing and removes them during idle periods. This is critical for optimizing costs and performance, ensuring that compute resources are consumed only when necessary without requiring manual intervention by the engineer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It forces the cluster to always use 8 nodes to ensure maximum performance.
Why it's wrong here
The autoscale setting defines a range rather than a fixed number of nodes. It allows the cluster to scale between 2 and 8 workers dynamically. Forcing 8 nodes at all times would result in higher compute costs without necessarily improving performance if the job does not require it.
- ✓
It enables the cluster to adjust resources dynamically based on current job load.
Why this is correct
Autoscaling provides the flexibility to increase or decrease worker node counts based on the volume of data being processed. This capability is essential for balancing job execution speed and resource costs, as it allows the platform to react automatically to the varying demands of Spark tasks.
- ✗
It ensures that the cluster uses specific instance types defined in the policy.
Why it's wrong here
The node_type_id attribute specifies the hardware instance type used by the cluster, not the autoscaling logic. While policy_id ensures the configuration adheres to specific organizational standards, the autoscaling attribute itself focuses strictly on node quantity and workload responsiveness, not on the underlying hardware instance hardware type.
- ✗
It mandates the use of Spot Instances to reduce total cost of ownership.
Why it's wrong here
Autoscaling adjusts the number of nodes but does not specify the purchasing model for those nodes. Spot instances would be configured separately, often through specific cluster or instance pool settings. The provided JSON includes no directives for using Spot or On-Demand instances, only node count ranges.
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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
This Databricks-DE-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-DE-Assoc exam.