Databricks DA-ASSOC exam guide covering SQL, dashboards, Genie, modeling, and data security decisions.
This guide targets Databricks Certified Data Analyst Associate (DA-ASSOC), Databricks’ analyst-level certification for SQL-first work on the Databricks Data Intelligence Platform. As of April 13, 2026, Databricks’ live certification page says the exam has 45 scored multiple-choice questions, a 90-minute time limit, a $200 registration fee, English delivery, and 2-year validity. Databricks’ current certification page and the Oct. 30, 2025 exam guide both use a 9-domain blueprint, and this guide follows that structure directly.
SQL Warehouse: Databricks compute service used to run Databricks SQL queries, dashboards, alerts, and Genie-backed analysis.
Certified dataset: Governed data asset in Unity Catalog that analysts can treat as a trusted starting point.
Trusted asset: Curated SQL query or function inside a Genie space that Databricks can use as a higher-confidence answer path.
| Exam fact | Current official value |
|---|---|
| Scored questions | 45 multiple-choice |
| Time limit | 90 minutes |
| Registration fee | $200 |
| Delivery | online or test center |
| Question type | multiple choice |
| SQL expectation | ANSI SQL |
| Recommended experience | 6+ months of hands-on data analyst work on Databricks |
| Validity | 2 years |
| Guide model | 9 blueprint chapters -> 17 section lessons |
DA-ASSOC does not reward raw syntax recall by itself. Strong answers usually start by classifying the question into the right lane first: governed data discovery, data import, query authoring, query analysis, dashboard behavior, Genie configuration, data modeling, or security boundary. The trap is often not missing a keyword. The trap is solving the wrong layer of the Databricks workflow.
The current Databricks certification page publishes domain weights for DA-ASSOC. This guide follows that map directly.
| Exam domain | Weight | Chapter | Start here |
|---|---|---|---|
| Understanding of Databricks Data Intelligence Platform | 11% | 1. Platform | 1.1 Core & Governance, 1.2 Catalog & Marketplace |
| Managing Data | 8% | 2. Data | 2.1 Cleaning & Quality, 2.2 Lineage & Discovery |
| Importing Data | 5% | 3. Import | 3.1 Uploads, Sharing & Auto Loader |
| Executing queries using Databricks SQL and Databricks SQL Warehouses | 20% | 4. SQL | 4.1 Warehouses & Authoring, 4.2 Joins, Aggregations & Federation, 4.3 Views, Tables & Time Travel |
| Analyzing Queries | 15% | 5. Analysis | 5.1 Photon, Profile & Caching, 5.2 Clustering, History & Results |
| Creating Dashboards and Visualizations in Databricks | 16% | 6. Dashboards | 6.1 Dashboards & Parameters, 6.2 Sharing, Refresh & Alerts |
| Developing, Sharing, and Maintaining AI/BI Genie spaces | 12% | 7. Genie | 7.1 Build Genie Spaces, 7.2 Permissions & Tuning |
| Data Modeling with Databricks SQL | 5% | 8. Modeling | 8.1 Model Fit |
| Securing Data | 8% | 9. Security | 9.1 UC Roles & Sharing, 9.2 Ownership, PII & Storage |
flowchart LR
A["1. Platform, Unity Catalog, and data import"] --> B["2. Query authoring and result correctness"]
B --> C["3. Query analysis, dashboards, and alerts"]
C --> D["4. Genie spaces, modeling, and security"]
D --> E["Cheat sheet, glossary, FAQ, and live Databricks checks"]
| Failure pattern | Better instinct |
|---|---|
| trying to fix wrong numbers in the dashboard instead of the query | repair row grain, join logic, or window logic first |
| mixing managed tables, external tables, Delta Sharing, and Marketplace into one blur | classify storage, governance, and distribution as separate choices |
| using the same answer for query execution and query analysis | write the query first, then inspect warehouse, profile, cache, or clustering behavior |
| treating Genie like a replacement for dataset curation | good Genie answers still depend on curated datasets, instructions, and trusted assets |
| answering security questions with generic SQL knowledge only | restate catalog, schema, object, owner, permission, and sharing boundary before picking the answer |