DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your team uses Azure Databricks for data processing. You need to implement a cost-control strategy that automatically terminates idle clusters after 30 minutes of inactivity, but allows users to override this policy for specific workloads that require long-running clusters. What is the most efficient approach?
⚠ Common exam trap
DP-203 often tests the difference between a hard global limit (which removes flexibility) and a policy-based default with override (which balances governance and flexibility) — candidates who pick the global setting miss the override requirement.
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
✓
Create a cluster policy that enforces auto-termination with a default of 30 minutes, but allows users to override the value for specific clusters.
Azure Databricks cluster policies let administrators define constraints and defaults for cluster configuration, including auto-termination. A cluster policy can set a default auto-termination of 30 minutes while allowing users to override the value (within policy-permitted bounds) for specific long-running workloads. This centralizes governance without blocking legitimate exceptions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instruct all users to set auto-termination to 30 minutes on each cluster they create.
Why it's wrong here
Manual per-cluster configuration relies on user compliance and cannot be overridden centrally, so idle clusters escape termination whenever users forget. Setting auto-termination individually is legitimate for ad-hoc clusters, but the requirement needs a workspace-level policy with an override path.
- ✗
Configure a global auto-termination setting in the Azure Databricks workspace that terminates all clusters after 30 minutes of inactivity.
Why it's wrong here
A workspace-level auto-termination setting applies uniformly and offers no supported per-cluster override, so long-running workloads would be killed. Global defaults suit environments where every cluster should share one idle threshold, but the stem explicitly requires an exception mechanism for specific workloads.
- ✗
Use Azure Policy to enforce a tag that triggers a function to terminate idle clusters.
Why it's wrong here
Azure Policy governs resource tags and compliance, not Databricks cluster lifecycle; a tag-triggered function cannot terminate idle clusters reliably. Tag enforcement is correct for governance and cost allocation, yet auto-termination must be configured through Databricks cluster policies or workspace settings.
- ✓
Create a cluster policy that enforces auto-termination with a default of 30 minutes, but allows users to override the value for specific clusters.
Why this is correct
A cluster policy sets auto-termination at 30 minutes by default while permitting overrides, so idle clusters terminate automatically yet long-running workloads can extend the timeout. This satisfies both the cost-control and override requirements without manual intervention.
Go deeper
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Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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 Microsoft exam blueprint
This DP-203 practice question is part of Courseiva's free Microsoft 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 DP-203 exam.