Databricks Data Analyst Associate study plan
Five weeks at 6–8 hours a week. You need a Databricks workspace with Databricks SQL access and some data to query — nine sections is a lot of surface, and most of it is hands-on.
The plan groups the nine sections rather than treating each individually.
Week 1: the platform (11%), managing and importing data (13%)
The foundational sections, 24% together.
- The Databricks Data + AI Platform: the lakehouse idea, Delta tables, how the workspace is organised, and where Databricks SQL sits within it.
- SQL warehouses: what they are, how they differ from all-purpose compute, sizing, and the fact that they cost money while running.
- Managing data: catalogs, schemas and tables; creating and altering them; managed versus external tables.
- Importing data: loading files and connecting sources. Only about two questions — cover it and move on.
Week 2: executing queries (20%)
The largest section.
- Writing SQL in Databricks SQL: joins, aggregations, subqueries, common table expressions.
- Window functions — ranking, running totals, period comparisons. Common in analyst work and common on the exam.
- Query history and understanding why a query was slow.
- Caching and result reuse.
- Warehouse sizing and its effect on query performance and cost.
Week 3: analyzing queries (15%) and data modelling (5%)
- Interpreting results: does this number look right, and how would you check?
- Diagnosing a slow or expensive query from its profile.
- Spotting logical errors — the join that duplicated rows, the filter applied after the aggregate.
- Basic optimisation: filtering early, avoiding unnecessary scans.
- Data modelling with Databricks SQL: views, materialised views, and when each is appropriate. Only two questions, but the view-versus-materialised-view distinction is reliably tested.
Week 4: dashboards (16%) and Genie spaces (12%)
28% together, and the most distinctly analyst part of the exam.
Dashboards and visualisations:
- Building dashboards, choosing appropriate chart types, and using parameters and filters.
- Scheduled refresh, and what that costs.
- Sharing dashboards and controlling who sees what.
AI/BI Genie spaces:
- What a Genie space is: a curated area where business users ask questions of data in natural language.
- Setting one up: which tables to include, and the instructions and context that make answers reliable.
- Maintaining it — reviewing questions asked, correcting bad answers, adding context.
- Sharing and permissions.
Spend real time here. It is 12% and it is the section most analysts have never used.
Week 5: securing data (8%), then practice
- Unity Catalog permissions from an analyst’s perspective: what you can see and why.
- Row and column level restrictions, and masking.
- Sharing data and dashboards safely.
Then: a full practice exam under real conditions, mistakes sorted by section, weakest rebuilt, second practice exam.
Where the hours go
| Week | Focus | Weight | Hours |
|---|---|---|---|
| 1 | Platform, managing and importing data | 24% | 6–8 |
| 2 | Executing queries | 20% | 6–8 |
| 3 | Analyzing queries and modelling | 20% | 6–8 |
| 4 | Dashboards and Genie spaces | 28% | 6–8 |
| 5 | Securing data and practice | 8% | 6–8 |
The two things most people get wrong
Neglecting Genie spaces. It is 12% — around five questions — and unfamiliar to most analysts. That is more expensive than a weak area you at least half know.
Treating it as a pure SQL exam. Query writing is 20%. The other 80% is the platform, dashboards, Genie, governance and interpretation. Strong SQL alone does not pass this.
Build one dashboard with a parameter, and one Genie space over two tables. Both take an hour and both cover sections you cannot learn from reading.