Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst has a notebook that reads a Delta table and produces summary statistics. The analyst wants colleagues to see the latest results in a dashboard without rerunning the notebook manually each time. Which Databricks capability should the analyst use to keep the dashboard current?
⚠ Common exam trap
The trap here is treating a notebook visualization as automatically shared and refreshed, when dashboards require scheduled queries to stay current without manual runs.
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
✓
Convert the notebook into a Databricks SQL dashboard and schedule the underlying query
Databricks SQL dashboards are built on saved queries that can be scheduled to refresh, so viewers see current results without anyone rerunning a notebook. Recreating the summary logic as a query and scheduling it automates currency. Documentation, larger clusters, and static exports do not refresh a dashboard, so they fail to meet the requirement of up-to-date shared results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a markdown cell describing how to run the notebook
Why it's wrong here
A markdown cell only documents instructions; it does not refresh data or update a dashboard. Colleagues would still need to run the notebook themselves and then regenerate any visuals. This approach fails to automate currency, so it does not satisfy the requirement that the dashboard reflect the latest results without manual intervention.
- ✗
Increase the cluster size used by the notebook
Why it's wrong here
Larger compute can speed up a single run but does nothing to schedule or refresh a dashboard. The problem is not performance but automation and delivery of updated results. Scaling the cluster leaves the manual rerun requirement intact, so it does not meet the scenario's goal of keeping the dashboard current automatically.
- ✓
Convert the notebook into a Databricks SQL dashboard and schedule the underlying query
Why this is correct
A Databricks SQL dashboard is backed by saved queries that can be scheduled to refresh, so viewers see updated results without manual notebook runs. Migrating the summary logic into a query and scheduling it keeps the dashboard current automatically. This matches the goal of sharing live results while removing the need for manual execution.
- ✗
Export the notebook results to a static PDF for distribution
Why it's wrong here
A static PDF captures a point-in-time snapshot and cannot update itself as data changes. Distributing it would give colleagues stale numbers and still require someone to regenerate the export. Because the requirement is an automatically current dashboard, a static export is the wrong mechanism and does not address the need.
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.