Databricks Data Analyst: platform and data
Understanding of the Databricks Data + AI Platform (11%), Managing Data (8%) and Importing Data (5%) are 24% of the Data Analyst Associate exam between them — around 11 of the 45 scored questions.
These are the foundations. None is large individually, but together they are nearly a quarter of the paper, and they underpin every other section.
Understanding the platform (11%)
- The lakehouse idea — one storage layer serving analytics and engineering, rather than a warehouse kept in sync with a lake.
- Delta tables as the default format, and what that gives you: ACID transactions, schema enforcement, time travel.
- How the workspace is organised: catalogs, schemas, tables, dashboards, queries.
- Where Databricks SQL sits — the analyst-facing surface.
SQL warehouses deserve particular attention:
- They are the compute that runs your queries, distinct from all-purpose clusters.
- Size affects query speed and cost. Bigger is faster and more expensive, and not always the right answer.
- Auto-stop shuts a warehouse down when idle. A warehouse left running overnight bills for nothing, and this is a recurring exam scenario.
- Scaling handles concurrency — many users at once — rather than making a single query faster.
The distinction the exam tests: size for query complexity, scaling for concurrency, auto-stop for cost.
Managing data (8%)
- The catalog → schema → table hierarchy.
- Creating and altering tables and views.
- Managed versus external tables: managed tables have their data lifecycle tied to the table, so dropping one deletes the data. External tables point at data you manage elsewhere, and dropping the table leaves the data. This is reliably tested.
- Basic table maintenance from an analyst’s perspective.
Importing data (5%)
About two questions. Cover it briskly:
- Loading files into tables through the UI and with SQL.
- Connecting to external data.
- Basic schema handling on load.
This exam is not about building ingestion pipelines. That is the Data Engineer Associate.
Sample questions
Question 1. A SQL warehouse is sized 2X-Large because occasionally a complex query is slow. Most of the day, thirty analysts run small queries and the warehouse sits mostly idle. What is the most cost-effective adjustment?
- A. Keep the 2X-Large warehouse running continuously for consistent performance
- B. Disable auto-stop so the warehouse is always warm
- C. Reduce the size, enable scaling for concurrency, and configure auto-stop
- D. Ask analysts to batch their queries into scheduled runs
Show answer
Answer: C
A smaller warehouse with scaling handles many concurrent small queries economically while auto-stop removes idle cost, matching the actual usage pattern. Keeping a large warehouse running wastes money, disabling auto-stop worsens it, and asking analysts to batch queries does not address the mismatch.
Question 2. An analyst drops a managed table, believing only the table definition will be removed. What actually happens?
- A. Both the table definition and the underlying data are removed
- B. Only the table definition is removed; the data remains
- C. The table is archived and can be restored from the UI indefinitely
- D. The operation fails because managed tables cannot be dropped
Show answer
Answer: A
Dropping a managed table removes the underlying data as well, because a managed table’s data lifecycle is tied to the table. An external table would leave the data in place, which is the distinction being tested.
Question 3. An organisation keeps raw files in its own cloud storage and wants a Databricks table over them without Databricks taking ownership of the files' lifecycle. What should be created?
- A. A managed table
- B. A temporary view
- C. A copy of the files imported into a managed table
- D. An external table over the existing storage location
Show answer
Answer: D
An external table references data in a location the organisation manages, so dropping the table does not delete the files. A managed table would tie the data lifecycle to the table, a temporary view does not persist, and copying the files duplicates storage.
What to practise
Create one managed table and one external table, then drop both and see what survives. Size a SQL warehouse down and enable auto-stop, then check what it costs you overnight.
Two short exercises covering the most testable ideas across all three sections.