Databricks-DE-Pro Cost and Performance Optimization Practice Question
Exhibit
{
"instance_pool_id": "pool-0412-182230-cried5",
"min_idle_instances": 2,
"max_capacity": 10,
"node_type_id": "i3.xlarge"
}Refer to the exhibit. A data engineer creates an instance pool to reduce cluster startup times for development teams. However, finance reports indicate unexpected cloud infrastructure charges. Based on the configuration shown in the exhibit, what is the primary driver of these unexpected costs?
⚠ Common exam trap
Candidates often blame 'cluster size' or 'DBU usage' for costs, missing the specific detail that 'min_idle_instances' forces the cloud provider to keep virtual machines running 24/7.
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 min_idle_instances setting maintains running virtual machines continuously, incurring persistent infrastructure costs.
Instance pools maintain idle virtual machines ready for immediate attachment to clusters, drastically reducing startup latency. However, setting min_idle_instances to 2 ensures that two instances are running constantly even when no clusters are active, incurring continuous cloud provider infrastructure charges and idle DBU fees that accumulate rapidly.
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 max_capacity limit is set too low, forcing teams to provision multiple competing instance pools.
Why it's wrong here
A max_capacity of 10 limits the maximum number of instances the pool can hold. While restrictive for large teams, it caps rather than increases infrastructure spending and does not explain unexpected continuous baseline charges during idle times.
- ✗
Instance pools do not support Spot instances, forcing all pool-backed clusters to run expensive on-demand VMs.
Why it's wrong here
Databricks instance pools fully support spot instances when configured correctly. The unexpected cost is not caused by an inability to use spot pricing, but rather by the resource allocation strategy maintaining active idle virtual machines.
- ✓
The min_idle_instances setting maintains running virtual machines continuously, incurring persistent infrastructure costs.
Why this is correct
min_idle_instances keeps that number of virtual machines powered on at all times, even when no clusters are running. Those idle instances bill continuously for cloud infrastructure, which is the persistent cost driver behind the unexpected charges shown in the exhibit.
- ✗
The selected node_type_id is optimized for storage rather than compute, causing inflated licensing surcharges.
Why it's wrong here
Instance types like i3.xlarge are storage-optimized instances commonly used for caching and shuffle-heavy workloads. While they have specific pricing structures, storage optimization does not inherently trigger unexpected persistent idle billing compared to minimum idle node counts.
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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.