DP-203 Develop data processing Practice Question
You are designing a data processing solution that uses Azure Databricks to transform large datasets. You need to ensure that the processing is cost-effective and can scale to handle variable workloads. Which cluster configuration should you recommend?
⚠ Common exam trap
It's easy for candidates to assume premium tier or Photon acceleration automatically improves cost-effectiveness, but these features address performance or governance, not the core requirement of scaling with variable workloads and minimizing cost via spot pricing.
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
✓
Use an auto-scaling cluster with spot instances.
Auto-scaling clusters in Azure Databricks dynamically adjust the number of workers based on workload demands, ensuring cost-effectiveness by scaling down during low activity. Spot instances (Azure Spot VMs) further reduce costs by using unused Azure capacity at a significant discount, making this combination ideal for variable workloads where fault tolerance is acceptable.
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 an auto-scaling cluster with spot instances.
Why this is correct
Auto-scaling adjusts worker count to match variable workload demand, while spot instances cut compute cost substantially for fault-tolerant Spark jobs. Together they satisfy the cost-effectiveness and variable-scale constraints, provided spot eviction is tolerated by the transformation workload.
- ✗
Use a fixed-size cluster with premium tier.
Why it's wrong here
A fixed-size cluster cannot add or remove workers as workload varies, so it is over-provisioned at troughs and under-powered at peaks. Fixed sizing suits steady, predictable throughput; autoscaling job clusters are needed when demand fluctuates.
- ✗
Use a Photon-accelerated cluster with premium tier.
Why it's wrong here
Photon accelerates query execution but does not provide the elasticity that variable workloads require; a premium tier adds governance features, not autoscaling. Photon suits consistently heavy SQL and DataFrame workloads where raw speed, not scaling, is the constraint.
- ✗
Use an interactive cluster with a large number of workers.
Why it's wrong here
Interactive clusters stay running to serve notebooks, so idle workers bill continuously and cannot elastically match variable workloads. They suit collaborative development and exploratory analysis, not scheduled production transformations where job clusters terminate after each run.
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