Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is reviewing a Databricks job that runs a notebook to process a large Delta table. The job takes 45 minutes, and the engineer notices that the cluster spends a significant amount of time in the 'Pending' state before execution begins. The cluster is a job cluster with autoscaling enabled and no cluster pool. The engineer wants to reduce the overall job duration and cost. Which action should the data engineer take?
⚠ Common exam trap
The trap here is assuming that autoscaling settings affect initial cluster startup, when in fact they only control scaling after the cluster is running.
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 a cluster pool and attach the job cluster to it so that instances are pre-provisioned and startup time is reduced.
The 'Pending' state indicates the cluster is waiting for cloud instances to be provisioned. A cluster pool pre-provisions instances so they are ready when the job starts, which reduces the time spent waiting and shortens the overall job duration. While pools have some idle cost, for a frequently running job with significant startup overhead, the reduction in runtime and the ability to use right-sized clusters often results in net savings. Increasing max workers or reducing min workers does not address provisioning delay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum number of workers in the autoscaling configuration to 32 so the cluster scales up faster.
Why it's wrong here
Increasing the maximum worker count does not speed up the initial cluster provisioning; it only allows more workers to be added later if autoscaling decides to scale. The 'Pending' state is caused by waiting for the initial instances, not by a lack of maximum capacity. This change may increase cost without reducing startup time.
- ✗
Switch the job to use a serverless job compute environment, which eliminates cluster startup time.
Why it's wrong here
Serverless job compute can reduce startup time, but it is not available for all workloads and may have limitations on libraries, instance types, and networking. The scenario does not state that serverless is supported for this job. While it can be a valid optimization, it is not a guaranteed fix without confirming compatibility, and the question asks for an action the engineer should take based on the given information.
- ✓
Enable a cluster pool and attach the job cluster to it so that instances are pre-provisioned and startup time is reduced.
Why this is correct
Cluster pools keep a set of idle instances ready, so when the job starts, the cluster can acquire instances quickly instead of waiting for cloud VM provisioning. This directly reduces the 'Pending' time and shortens the job duration. The pool does incur some idle cost, but for a job that runs frequently and has significant startup overhead, the reduction in runtime and the ability to use smaller clusters can offset that cost.
- ✗
Reduce the cluster's autoscaling minimum workers to 1 to lower cost, and accept the longer startup.
Why it's wrong here
Reducing the minimum workers lowers cost but does not address the 'Pending' time; in fact, it may increase the time to reach the required capacity because the cluster starts smaller and must scale up. The goal is to reduce both duration and cost, and this action only reduces cost at the expense of duration. It does not solve 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.