Databricks-DE-Pro Cost and Performance Optimization Practice Question
A Databricks SQL warehouse is experiencing high costs due to idle resources. Which TWO configurations should be implemented to effectively manage and reduce warehouse costs?
⚠ Common exam trap
Exam takers often recommend cluster scaling down or increasing instance sizes to reduce SQL warehouse costs, overlooking that serverless auto-stop duration and query optimization are the primary levers.
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
✓
Set the auto-stop duration to a very low value, such as 1 minute, for serverless SQL warehouses.
Auto-stop and serverless scaling are the primary levers for cost control in Databricks SQL. Auto-stop terminates warehouses when no queries are running, preventing billing for idle time. Serverless SQL warehouses provide faster startup times and more granular scaling, allowing users to configure aggressive auto-stop durations without compromising user experience. Together, these configurations ensure that compute resources are only consumed when active query processing is required, directly minimizing unnecessary cloud spending.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the auto-stop duration to a very low value, such as 1 minute, for serverless SQL warehouses.
Why this is correct
Serverless SQL warehouses support very fast startup times, making a 1-minute auto-stop duration feasible. This minimizes the period that the warehouse remains active while idle, ensuring that billing stops almost immediately after the last query finishes, which is highly effective for reducing costs in environments with intermittent usage.
- ✗
Enable multi-cluster load balancing to ensure all queries are executed on the largest instance type.
Why it's wrong here
Multi-cluster load balancing is designed to increase concurrency by spinning up additional clusters, not to force execution on the largest instance type. Using the largest instance type unnecessarily increases costs and does not guarantee improved performance for smaller queries that might be better served by smaller, cost-effective clusters.
- ✗
Configure SQL warehouse scaling to use the maximum cluster size at all times to avoid resizing overhead.
Why it's wrong here
Setting a warehouse to the maximum size at all times forces the highest billing rate regardless of current workload demands. This is a poor cost-optimization strategy, as it ignores the elasticity benefits of Databricks SQL, leading to significant over-provisioning and wasted budget during periods of low or moderate activity.
- ✓
Implement SQL query history monitoring to identify and optimize long-running or resource-intensive queries.
Why this is correct
Identifying expensive queries through the query history allows engineers to optimize code, improve filter usage, or adjust data partitioning. By reducing the total compute time required for these heavy jobs, the warehouse consumes fewer resources, enabling shorter auto-stop timers and lowering overall operational costs for the Databricks SQL environment.
- ✗
Disable the query cache to ensure all results are freshly computed for accurate billing metrics.
Why it's wrong here
Disabling the query cache forces the warehouse to recompute results for every query, which significantly increases compute usage and costs. The query cache is a performance feature designed to return results instantly for identical queries, saving both time and money by avoiding unnecessary re-execution of complex operations.
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
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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.