Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is configuring a Delta Live Tables (DLT) pipeline that processes streaming data from Apache Kafka. The pipeline performs a series of transformations and writes to a Delta table. The engineer notices that the pipeline is experiencing high latency and wants to optimize it for cost and performance. The pipeline is set to continuous mode. Which configuration change is most effective to reduce cost while maintaining acceptable latency?
⚠ Common exam trap
The trap here is assuming that performance optimizations like increasing partitions or using Photon will reduce cost, when they may actually increase it due to higher resource consumption or DBU rates.
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
✓
Switch the pipeline to triggered mode and schedule it to run every 5 minutes.
Switching from continuous to triggered mode with a 5-minute schedule reduces cost by allowing the cluster to shut down between runs. Continuous mode keeps the cluster running 24/7, incurring constant DBU charges. Triggered mode with a schedule balances latency and cost, making it the most effective change for reducing cost while maintaining acceptable latency.
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 number of Kafka partitions to improve parallelism.
Why it's wrong here
Increasing Kafka partitions can improve throughput but also increases the number of tasks and potentially the cluster size needed, which may raise cost. It does not directly address the cost of the DLT pipeline itself. The latency issue might be due to other factors, and simply adding partitions may not reduce cost; it could even increase it if the cluster scales up to handle more partitions.
- ✗
Configure the pipeline to use Photon-optimized clusters.
Why it's wrong here
Photon can improve performance but comes at a higher DBU rate. While it might reduce runtime, the cost per DBU is higher, so overall cost may not decrease. For a continuous pipeline, the cluster runs constantly, so the higher rate could actually increase cost. Photon is beneficial for performance-critical workloads, but not necessarily for cost reduction in this scenario.
- ✓
Switch the pipeline to triggered mode and schedule it to run every 5 minutes.
Why this is correct
Triggered mode runs the pipeline only when triggered, rather than continuously. Scheduling every 5 minutes can reduce cost because the cluster is not always running, while still providing acceptable latency for many use cases. This is a common cost-saving measure for streaming pipelines that do not require sub-second latency. It also allows the cluster to shut down between runs, saving DBUs.
- ✗
Enable autoscaling on the DLT cluster and set a minimum number of workers.
Why it's wrong here
Autoscaling can help with performance but may not reduce cost if the cluster often scales to the maximum. Setting a minimum number of workers ensures the cluster is always running at least that many, which could increase cost. Autoscaling is reactive and may not address the continuous mode's constant resource consumption. It is not the most effective cost-saving change for a continuous pipeline.
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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.