Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is reviewing a Databricks job that runs on a job cluster and reads a large Delta table. The engineer notices that the job takes a long time to start because the cluster is provisioned from scratch each time. The engineer wants to reduce the startup time without increasing cost significantly. Which action should the engineer take?
⚠ Common exam trap
Candidates often confuse cluster startup time with job execution time; features like AQE or larger drivers improve execution but not provisioning.
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
✓
Enable cluster pools and configure the job to use a pool.
Cluster pools keep a set of idle instances ready, so job clusters can start almost instantly by borrowing from the pool. This reduces startup time significantly. The other options either do not address startup time or could increase cost without solving the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger driver node to speed up initialization.
Why it's wrong here
A larger driver node provides more memory and compute for the driver but does not significantly reduce the time to provision worker nodes. The startup delay is primarily due to acquiring and configuring instances, not driver performance. This change would increase cost without solving the problem.
- ✓
Enable cluster pools and configure the job to use a pool.
Why this is correct
Cluster pools maintain a set of idle, ready-to-use instances that can be quickly allocated to clusters. Using a pool for the job cluster reduces startup time because the instances are already provisioned. It can also reduce cost by sharing idle instances across multiple clusters, though there is a small cost for maintaining the pool.
- ✗
Set the cluster's Spark configuration to enable adaptive query execution.
Why it's wrong here
Adaptive query execution (AQE) optimizes query plans at runtime and can improve job performance, but it does not affect cluster startup time. Enabling AQE is beneficial for execution but irrelevant to the provisioning delay. It would not reduce the time spent waiting for instances to be ready.
- ✗
Increase the cluster's autoscaling maximum to allow more nodes.
Why it's wrong here
Increasing the autoscaling maximum allows the cluster to add more nodes during execution but does not affect startup time. The cluster still needs to provision the initial nodes from scratch. This change could increase cost if the cluster scales up unnecessarily, without addressing the startup delay.
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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.