Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is tasked with reducing compute costs for an interactive SQL analytics workspace that runs sporadic, highly unpredictable queries. The jobs experience cold start delays and occasional out-of-memory errors due to sudden concurrency spikes. Which TWO strategies should the engineer implement to balance cost efficiency and performance?
⚠ Common exam trap
Candidates often choose manual cluster scaling policies or standard instance types, failing to recognize that serverless compute and Photon are designed for unpredictable, high-concurrency analytical workloads.
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
✓
Migrate interactive SQL workloads to Databricks Serverless Compute to dynamically scale resources and eliminate idle billing.
Implementing serverless compute eliminates idle cluster costs while absorbing concurrency spikes instantly through elastic scaling. Enabling Photon on standard clusters provides vectorized query execution that speeds up scans and aggregations, reducing runtime and lowering total execution costs. Together, these choices optimize both resource utilization and user concurrency responsiveness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure single-node clusters with maximum autoscaling limits to handle unpredictable concurrency peaks without cluster management overhead.
Why it's wrong here
Single-node clusters completely lack distributed processing capabilities, restricting query execution to a single driver and causing immediate out-of-memory failures on large datasets. They cannot scale out workloads across multiple worker nodes to handle concurrency effectively.
- ✓
Migrate interactive SQL workloads to Databricks Serverless Compute to dynamically scale resources and eliminate idle billing.
Why this is correct
Serverless SQL warehouses scale automatically with query concurrency and bill only while active, directly addressing the sporadic, unpredictable workload and idle-cost constraint. Cold starts and out-of-memory errors from concurrency spikes are absorbed by dynamic resource provisioning rather than fixed cluster sizing.
- ✗
Provision pools of pre-warmed idle driver nodes to ensure zero-second startup latency for all analytical queries.
Why it's wrong here
Pre-warmed idle driver nodes do not address concurrency spikes, since workers execute queries, and idle drivers waste cost. It is tempting because eliminating cold starts sounds attractive, and would suit predictable latency-sensitive workloads, not sporadic unpredictable analytics needing elastic worker scaling.
- ✓
Enable Photon acceleration on clusters executing heavy relational scans and complex analytical joins.
Why this is correct
Photon uses a vectorized execution engine written in C++ to optimize CPU cache utilization and memory bandwidth, significantly accelerating scan and join heavy workloads. Faster execution directly translates to lower compute costs on standard multi-node clusters.
- ✗
Disable automatic cluster termination and keep all worker nodes running 24/7 to guarantee immediate resource availability.
Why it's wrong here
Disabling auto-termination causes clusters to run continuously, racking up massive idle charges during off-peak hours and weekends. This is an anti-pattern for cost optimization in cloud environments where resources should scale down when not in use.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
About these practice questions
Courseiva writes every Databricks-DE-Pro question from scratch — 267 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.