Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineering team experiences massive compute waste because interactive development notebooks are frequently left running overnight by engineers. As a Databricks administrator, which configuration should you implement at the cluster policy level to automatically mitigate this financial exposure without disrupting ongoing development work?
⚠ Common exam trap
Candidates frequently choose 'cluster termination settings' at the workspace level or assume manual user training is sufficient, failing to realize that cluster policies are the mandatory enforcement mechanism.
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 a mandatory auto_termination_minutes rule in the cluster policy enforcing a maximum idle timeout threshold for all user-created clusters.
Enabling automatic cluster termination based on an idle timeout is the most direct policy enforcement mechanism to eliminate compute waste from forgotten interactive sessions. This setting continuously monitors the execution state of the driver and worker nodes, shutting down the cluster safely once the specified threshold is crossed, which drastically reduces cloud infrastructure spending while preserving user autonomy during active hours.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a maximum worker node limit constraint within the compute policy that prevents scaling beyond two nodes.
Why it's wrong here
Restricting the maximum number of worker nodes only limits the upper scale during peak processing loads, but it does nothing to stop a single-node or multi-node cluster from running indefinitely when users walk away from their keyboards without terminating the session manually.
- ✗
Enforce a strict maximum instance lifetime limit that terminates the cluster regardless of whether interactive queries or jobs are running.
Why it's wrong here
Enforcing a rigid maximum instance lifetime forcefully kills clusters even if an engineer is in the middle of a long-running interactive query or iterative debugging session, leading to frustrating work interruptions and lost unsaved state inside notebook cells.
- ✓
Set a mandatory auto_termination_minutes rule in the cluster policy enforcing a maximum idle timeout threshold for all user-created clusters.
Why this is correct
Mandating an idle termination timeout via cluster policies automatically reclaims compute resources whenever user activity ceases for a designated duration. It strikes the ideal balance by allowing full scaling flexibility during work hours while shutting down idle infrastructure automatically.
- ✗
Disable the ability for standard users to attach notebooks to interactive clusters, forcing all execution through scheduled jobs.
Why it's wrong here
Removing notebook attachment blocks interactive development entirely rather than reclaiming idle clusters, disrupting the ongoing work the policy must preserve. It is tempting because it eliminates overnight notebook sessions, and would suit environments where all execution is deliberately job-based.
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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.