Databricks DA Assoc Tests SQL, Dashboards and Genie on Unity Catalog — Not Spark Pipelines
The October 2026 refresh targets analysts who query, visualise and share governed data in Databricks SQL. Think like a consumer of curated tables: warehouses, AI/BI Dashboards, Genie and grants, not pipeline engineering.
Check Your Readiness →Most candidates understand Databricks Certified Data Analyst Associate concepts — and still fail. This exam tests how you apply knowledge under pressure.
Nine sections, with weights confirmed on the certification page. Expect SQL-reading items where you predict output (watch NULLs, join types and UNION versus UNION ALL) alongside design choices about which object, warehouse setting, dashboard feature or grant fits a reporting need.
Create a standard view or a streaming table
A materialized view stores the precomputed join and aggregation and refreshes on a schedule, matching the one-hour tolerance. A standard view recomputes on every read, and streaming tables suit append-only ingestion, not heavy aggregates.
Search warehouse query history for DELETE statements
Run DESCRIBE HISTORY on the table to see the operation, user and version, then query VERSION AS OF the previous version for the earlier totals. Query history lists queries run, not the table's committed changes.
LEFT JOIN orders, then WHERE orders.status = 'X'
A WHERE condition on the right table discards rows whose right columns are NULL, silently turning the left join into an inner join. Move the status condition into the ON clause to keep unmatched customers.
Create a second redacted copy of the customers table
Apply a column mask function to the phone column that checks group membership with is_account_group_member. Everyone queries the same table; a row filter would be wrong because it hides whole records, not values.
Warehouse size speeds individual heavy queries; the max cluster count absorbs concurrency. When the query profile shows high queue time with many users, raise max clusters. When it shows few files pruned, fix layout with liquid clustering rather than buying compute.
Filters narrow rows a dataset already returned. A parameter such as :lookback_days is substituted into the dataset SQL, so it can change aggregation windows or logic. Date-range parameters filter before aggregation; field filters never alter the computed numbers.
Published with embedded credentials, every query runs as the publisher, so all viewers see the publisher's rows and need no table grants. For managers to see only their region, publish without embedded credentials so each viewer's own identity and row filter apply.
A user needs USE CATALOG on main and USE SCHEMA on main.sales before SELECT on main.sales.orders works. Grants on a catalog or schema also cover tables created later, so choose the narrowest container that meets the request.
More tables give Genie more ambiguous choices. Accuracy improves with a small set of documented tables, column comments, instructions, and trusted assets for exact business definitions. Prove a change worked by rerunning benchmarks, not with a handful of spot checks.
Strengthen weak areas with exam-style practice questions and detailed explanations.
Simulate the real exam experience and assess your readiness under timed conditions.
Review key concepts, objectives, and exam topics in one place.
Get personalized explanations, learning recommendations, and instant answers.
Follow a structured learning path designed to help you prepare efficiently.
Test your SQL output reading, dashboard sharing and Genie curation against the October 2026 outline.