Databricks DA-ASSOC Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Databricks Data Analyst Associate (DA-ASSOC) topics such as Databricks SQL, Unity Catalog, dashboards, alerts, Genie spaces, data modeling, query analysis, and secure analytics workflow. The prompts emphasize trustworthy analytic results rather than syntax recall alone.

Where these questions fit in the DA-ASSOC guide

The sample set below is part of the Databricks DA-ASSOC guide path:

• Databricks certification hub
• DA-ASSOC exam guide
• DA-ASSOC cheat sheet
• DA-ASSOC study plan
• DA-ASSOC FAQ
• DA-ASSOC resources
• DA-ASSOC glossary

DA-ASSOC analytics sample questions

Work through each prompt before opening the explanation. DA-ASSOC questions usually reward answers that preserve row grain, use the correct Databricks SQL or dashboard layer, and keep permissions tied to Unity Catalog.

Question 1

Topic: Fixing duplicate totals

A dashboard total doubles after an analyst joins orders to order-line details. The dashboard query groups only by customer. What should the analyst check first?

• A. Whether the dashboard color palette can hide the doubled value.
• B. Whether the join changed the row grain and whether line-level rows need aggregation before joining to customer-level reporting.
• C. Whether the SQL warehouse should always be made larger when totals are wrong.
• D. Whether Genie can answer the question without curated source data.

Best answer: B

Explanation: Wrong totals after a join are often a row-grain problem. The analyst should validate the join path and aggregate to the intended reporting grain before visualizing.

Why the other choices are weaker:

• A hides the symptom instead of fixing the data logic.
• C treats a correctness issue as a compute issue.
• D does not remove the need for curated, trusted inputs.

What this tests: SQL joins, row grain, aggregation, dashboard correctness, and result validation.

Related topics: SQL; Row grain; Dashboards; Aggregation

Question 2

Topic: Choosing the analytics compute layer

A BI team needs shared compute for Databricks SQL queries, dashboards, and alerts. They want a managed analytics execution layer rather than an all-purpose notebook cluster. What should they use?

• A. A personal laptop running local CSV exports.
• B. A random single-user notebook cluster owned by one analyst.
• C. A SQL Warehouse sized and governed for the analytics workload.
• D. A dashboard parameter, because parameters execute SQL by themselves.

Best answer: C

Explanation: Databricks SQL Warehouses are the managed compute surface for SQL queries, dashboards, alerts, and BI-style analytics.

Why the other choices are weaker:

• A breaks governed shared analytics workflow.
• B creates ownership and operability problems.
• D controls query inputs, not compute execution.

What this tests: SQL Warehouse, dashboards, alerts, compute fit, and analytics operations.

Related topics: SQL Warehouse; Dashboards; Alerts; Compute

Question 3

Topic: Securing a shared dashboard

A dashboard should be visible to regional managers, but the underlying table includes columns and rows they should not all see. Which design is strongest?

• A. Export the table to a spreadsheet and ask managers not to open restricted rows.
• B. Give every manager ownership of the source table.
• C. Remove the dashboard title so sensitive data is harder to find.
• D. Use Unity Catalog permissions and governed views, row filters, column masks, or curated datasets appropriate to the access requirement.

Best answer: D

Explanation: Dashboard sharing depends on the underlying governed data access model. Unity Catalog permissions and curated access patterns enforce the boundary.

Why the other choices are weaker:

• A bypasses governance and relies on trust.
• B over-grants control of the object.
• C is obscurity, not access control.

What this tests: Unity Catalog, dashboard sharing, row filters, column masks, permissions, and governance.

Related topics: Unity Catalog; Security; Dashboards; Permissions

Independent study note

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