Free Databricks Data Analyst Associate questions
Try these before reading the answers. The mix follows the weighting, so querying, dashboards and Genie appear most.
Question 1. An analyst joins an orders table to a customers table and the row count increases from 50,000 to 73,000. Each order should appear once. What is the most likely cause?
- A. A missing WHERE clause on the orders table
- B. The SQL warehouse is undersized for the join
- C. The join key is not unique in the customers table, so orders match multiple rows
- D. An inner join was used instead of a left join
Show answer
Answer: C
A join that increases row count beyond the fact table indicates the join key is not unique on the dimension side, so each order matches several customer rows. A missing WHERE clause would not duplicate rows this way, warehouse size affects speed not results, and an inner join reduces rather than inflates row counts.
Question 2. Business users repeatedly ask the analytics team similar questions about the same two tables in natural language. What Databricks capability is designed for this?
- A. A dashboard scheduled to refresh hourly
- B. An AI/BI Genie space over the curated tables
- C. A larger SQL warehouse
- D. A materialised view over the two tables
Show answer
Answer: B
An AI/BI Genie space lets business users ask questions of curated tables in natural language, with context and instructions provided by the analyst. A scheduled dashboard answers fixed questions, a SQL warehouse is compute, and a materialised view precomputes results without providing a natural-language interface.
Question 3. A query aggregating a large table is run by dozens of users throughout the day, always with the same logic. Results only need to be current as of the previous night. What reduces cost and latency?
- A. A materialised view refreshed nightly
- B. A standard view over the same logic
- C. A larger SQL warehouse
- D. Asking users to run the query less frequently
Show answer
Answer: A
A materialised view precomputes and stores the aggregate so repeated queries read the result rather than rescanning the source, which suits identical logic and overnight freshness. A standard view re-executes each time, a larger warehouse costs more for the same repeated work, and asking users to run it less often does not address the cause.
Question 4. An analyst must publish a dashboard to a department, but one column in the underlying table contains salary data that department must not see. What is the correct approach?
- A. Hide the column in the dashboard’s visualisation settings
- B. Create a manual copy of the table without the salary column
- C. Add a note to the dashboard asking users not to query that column
- D. Apply column-level access control in Unity Catalog
Show answer
Answer: D
Restricting the column in Unity Catalog enforces the rule at the data layer, so it holds regardless of how the data is accessed. Hiding the column in the dashboard leaves the underlying data reachable, a manual copy drifts from source, and a written instruction enforces nothing.
Question 5. A SQL warehouse is left running continuously although queries only arrive during business hours. What is the appropriate change?
- A. Increase the warehouse size so queries finish faster
- B. Configure auto-stop so the warehouse shuts down when idle
- C. Delete the warehouse and recreate it each morning manually
- D. Ask users to batch their queries into one daily run
Show answer
Answer: B
Configuring auto-stop so the warehouse shuts down when idle removes the cost of compute nobody is using, which is the specific waste described. A larger warehouse increases cost, deleting it removes the service, and running queries less often does not address idle time.
How did you do?
Notice how few of these are pure SQL syntax. Query writing is 20% of the exam; the rest is platform, dashboards, Genie, governance and interpretation — which is why strong SQL alone does not carry it.
If question 2 was unfamiliar, that is the Genie section, worth 12%. See week 4 of the study plan.