Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst is preparing to publish a Databricks SQL dashboard for a team of business users. The analyst wants the dashboard to load quickly and to remain usable as the underlying Delta table grows. Which two practices should the analyst follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that keeping compute always on or moving data to CSV improves dashboard performance, when sizing, auto-stop, and pre-aggregation are the real 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
✓
Create the dashboard datasets against a SQL Warehouse that is sized appropriately and has auto-stop configured
Fast, scalable dashboards rely on appropriately sized SQL Warehouse compute with auto-stop and on reducing the data scanned through pre-aggregation such as summary tables or materialized views. Storing datasets as CSV, disabling auto-stop, and granting broad manage permissions do not improve performance and introduce cost or governance problems.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create the dashboard datasets against a SQL Warehouse that is sized appropriately and has auto-stop configured
Why this is correct
Sizing the SQL Warehouse appropriately ensures the dashboard queries have enough compute to return results quickly, and auto-stop controls cost when the dashboard is idle. This directly supports fast loading and sustainable operation as usage grows. The warehouse is the compute that serves dashboard queries, so choosing and configuring it correctly is a core best practice.
- ✗
Store the dashboard datasets as CSV files in a Unity Catalog volume for faster reads
Why it's wrong here
CSV files in a volume are not a performant or governed replacement for Delta tables in dashboard datasets. They lack Delta optimizations such as statistics, Z-ordering, and time travel, and they are not updated transactionally. Reading CSV also requires parsing and does not benefit from the Delta cache, so this would likely hurt performance rather than help.
- ✓
Pre-aggregate large fact tables into summary tables or materialized views used by the dashboard datasets
Why this is correct
Pre-aggregating large fact tables reduces the volume of data scanned when the dashboard loads, which improves response time and keeps the dashboard usable as data grows. Summary tables or materialized views can be refreshed on a schedule so the dashboard reads smaller, optimized datasets. This is a standard performance practice for dashboards over large Delta tables.
- ✗
Disable the SQL Warehouse auto-stop so the dashboard always has warm compute
Why it's wrong here
Disabling auto-stop keeps compute running continuously, which increases cost significantly and is not a recommended practice for most dashboards. While it can reduce cold-start latency, it does not address the growing data volume problem and can lead to unnecessary spend. Auto-stop should generally remain enabled, with sizing and pre-aggregation used to manage performance.
- ✗
Grant every viewer CAN MANAGE on the dashboard so they can tune queries themselves
Why it's wrong here
Granting CAN MANAGE to all viewers is a security and governance risk, as it allows them to change permissions, edit the dashboard, and potentially alter datasets. It does not improve performance and violates least privilege. Viewers typically need CAN VIEW or CAN RUN, while a small number of owners should hold CAN MANAGE.
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-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.