Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst is new to a Databricks workspace and needs to understand which compute options are available for running SQL and notebooks. Which TWO of the following statements accurately describe Databricks compute in this context? (Choose two.)
⚠ Common exam trap
The trap here is assuming there is a single compute type for all Databricks work, when SQL warehouses, job clusters, and all-purpose clusters serve distinct purposes.
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
✓
SQL warehouses are optimized for running SQL and serving dashboards and scheduled queries.
SQL warehouses are purpose-built for SQL, dashboards, and scheduled queries, while job clusters are ephemeral compute created for a specific run and terminated afterward. Together these describe two real compute behaviors an analyst should know. The other statements misstate requirements or capabilities, such as claiming all-purpose clusters are needed for the SQL editor or that serverless cannot use Unity Catalog.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SQL warehouses are optimized for running SQL and serving dashboards and scheduled queries.
Why this is correct
SQL warehouses are compute resources specifically tuned for SQL workloads, including BI dashboards, Databricks SQL queries, and scheduled reports. They separate storage from compute and can auto-stop when idle. For an analyst focused on SQL and visualization, a SQL warehouse is the appropriate compute choice, making this statement accurate.
- ✗
All-purpose clusters are required to run any query in the Databricks SQL editor.
Why it's wrong here
The Databricks SQL editor runs against SQL warehouses, not all-purpose clusters. All-purpose clusters support notebooks and general-purpose workloads, but they are not a prerequisite for SQL editor queries. Claiming they are required misstates how Databricks SQL compute is provisioned and would mislead a new analyst about available options.
- ✗
SQL warehouses and all-purpose clusters share the same configuration settings and scaling behavior.
Why it's wrong here
SQL warehouses and all-purpose clusters are distinct compute types with different sizing, scaling, and optimization characteristics. SQL warehouses focus on SQL concurrency and auto-stop, while clusters support varied languages and libraries. Treating them as identical misrepresents the platform and could lead an analyst to choose the wrong compute for a workload.
- ✓
A job cluster is created for a specific job run and terminated when the run completes.
Why this is correct
Job clusters are provisioned automatically for a scheduled job or notebook run and are torn down when the run finishes. This design isolates job workloads and reduces cost compared with leaving an all-purpose cluster running. It accurately describes how Databricks compute behaves for automated jobs, which is useful knowledge for a new analyst.
- ✗
Serverless compute for notebooks cannot be used with Unity Catalog.
Why it's wrong here
Serverless compute in Databricks is designed to work with Unity Catalog, providing governed access to data without managing cluster infrastructure. Saying it cannot be used with Unity Catalog contradicts how the platform is built. This incorrect statement would wrongly discourage an analyst from using serverless options in a governed workspace.
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
One of 291 original Databricks-DA-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-DA-Assoc 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-DA-Assoc exam.