20+ practice questions focused on Data Modeling with Databricks SQL — one of the most tested topics on the Databricks Certified Data Analyst Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Data Modeling with Databricks SQL PracticeWhich TWO of the following statements are true regarding the use of Delta Lake constraints in Databricks SQL?
Explanation: Delta Lake supports CHECK and NOT NULL constraints. CHECK constraints validate data during writes. NOT NULL constraints ensure columns do not contain nulls. However, Delta Lake does not enforce primary keys, and there is no automatic application of NOT NULL constraints to primary keys.
Which object type in Unity Catalog is most appropriate for organizing a set of related tables and views into a logical container that supports cross-schema queries?
Explanation: While a Schema is the correct answer for organizing tables and views, the explanation incorrectly states that Schemas are the primary container for organizing related tables across a catalog, while the prompt asks for a container that supports cross-schema queries. Furthermore, the explanation claims 'Catalog' is confused with 'Schema', but the hierarchy is Catalog > Schema > Table. The explanation should clarify that a Schema is the logical container for tables/views, and cross-schema queries are supported by referencing the three-level namespace (catalog.schema.table).
A retail analytics team maintains a Delta table named sales_transactions in Unity Catalog. They need to add a new column named loyalty_tier of type STRING to the table without affecting existing data or requiring a full rewrite. Which Databricks SQL command should they use?
Explanation: The correct command to add a new column to an existing Delta table in Databricks SQL is ALTER TABLE ... ADD COLUMN. This operation is lightweight and does not rewrite existing data. The other options either use incorrect syntax or attempt to modify a non-existent column. Thus, only the ADD COLUMN variant with singular COLUMN is valid.
A data analyst is designing a star schema in Databricks SQL and wants to ensure that dimension tables are easily discoverable and can be joined efficiently with fact tables. Which Unity Catalog feature should be used to organize related tables and provide a logical namespace?
Explanation: In Unity Catalog, schemas are the logical containers for tables and views. Placing all fact and dimension tables of a star schema in a single schema groups them under a common namespace, making them easier to find and manage. This is the recommended approach for organizing related data objects. Other options do not provide the same level of organization or discoverability.
A data analyst is working with a Delta table that contains a column 'timestamp' of type TIMESTAMP. The analyst needs to create a new table that includes an additional column 'date_key' derived from 'timestamp' as an integer in the format YYYYMMDD. The new table should be updated automatically whenever new data is inserted into the original table. Which approach should the analyst use?
Explanation: Generated columns in Delta Lake automatically compute values based on other columns when data is inserted. By defining date_key as a generated column with the expression CAST(DATE_FORMAT(timestamp, 'yyyyMMdd') AS INT), the new table will always have the correct date_key for each row upon insert. This is a native Databricks SQL feature that satisfies the automatic update requirement without additional orchestration.
+15 more Data Modeling with Databricks SQL questions available
Practice all Data Modeling with Databricks SQL questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Data Modeling with Databricks SQL. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Data Modeling with Databricks SQL questions on the Databricks-DA-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Data Modeling with Databricks SQL is tested as part of the Databricks Certified Data Analyst Associate blueprint. Practicing with targeted Data Modeling with Databricks SQL questions ensures you can handle any format or difficulty that appears.
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