Databricks-DE-Pro Cost and Performance Optimization Practice Question
Exhibit
{
"cluster_type": "all-purpose",
"autoscale": {
"min_workers": 2,
"max_workers": 20
},
"spark_version": "13.3.x-cpu-ml-scala2.12",
"node_type_id": "i3.xlarge",
"auto_termination_minutes": 120
}Refer to the exhibit. An administrator reviews the cluster configuration JSON for an all-purpose interactive development cluster used by data engineers. Based on Databricks cost and performance optimization best practices, which specific parameter in this configuration represents the highest risk for unnecessary financial expenditure?
⚠ Common exam trap
Candidates often focus on instance types or library versions. They fail to notice that an idle cluster running for two hours is a massive, avoidable cost leak in an interactive development environment.
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 auto_termination_minutes parameter is set to 120, allowing interactive compute to remain idle and bill at all-purpose rates for two full hours.
The exhibit shows an all-purpose cluster configured with a 120-minute auto-termination timeout and heavy ML-runtime packages on storage-optimized instance types. Setting auto-termination to 120 minutes (2 hours) of inactivity causes extreme financial waste when developers walk away. Interactive all-purpose clusters should typically timeout after 30 to 60 minutes of inactivity to optimize cost recovery.
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 spark_version string specifies an ML runtime version containing unnecessary packages for general data engineering workloads.
Why it's wrong here
An ML runtime bundles libraries such as GPU-accelerated ML frameworks, but these inflate only startup time and image size, not the dominant cost driver. It is tempting because runtime choice does affect cluster provisioning, yet for an all-purpose interactive cluster the real financial risk is an auto-termination setting that is absent or excessive.
- ✗
The node_type_id specifies i3.xlarge storage-optimized instances which are entirely unsupported for running standard Delta Lake queries.
Why it's wrong here
i3.xlarge instances are supported for Delta Lake workloads; the real cost risk lies in fixed-size or oversized clusters without autoscaling or auto-termination. It is tempting because storage-optimised instance families can be wasteful for compute-heavy queries, and would be correct where the workload is genuinely compute-bound.
- ✓
The auto_termination_minutes parameter is set to 120, allowing interactive compute to remain idle and bill at all-purpose rates for two full hours.
Why this is correct
Idle interactive compute bills at the higher all-purpose DBU rate, so a 120-minute auto_termination window leaves two hours of chargeable inactivity per session. Shortening it to 10–30 minutes directly satisfies the stem's cost-optimisation constraint, since idle time, not cluster size, drives the unnecessary expenditure here.
- ✗
The autoscale max_workers limit is capped at 20, which will instantly crash any query exceeding ten gigabytes of data volume.
Why it's wrong here
Capping max_workers at 20 limits horizontal scaling; it does not crash queries at ten gigabytes, since worker count and data volume are unrelated. The parameter is tempting because autoscale ceilings do govern cost, but the genuine expenditure risk lies in an idle, always-on cluster or an oversized fixed worker count, not this cap.
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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-Pro 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-Pro exam.