Write correct Databricks SQL DDL for tables and views, enable and reason about Change Data Feed, and choose between Liquid Clustering and partitioning. The key is knowing which layout or feature matches the stated performance, lifecycle, and access requirement.
Start practicing
Data Modeling with Databricks SQL — choose a session length
Free · No account required
Domain overview
This domain covers designing and governing tables, views, and data layout in Databricks SQL on Delta Lake and Unity Catalog. Questions test Change Data Feed, view creation and permissions, Liquid Clustering, and partitioning trade-offs, usually as scenario-based multiple-choice items where you pick the correct SQL statement or diagnose a configuration error.
Exam objectives
Enabling Change Data Feed on Delta tables and interpreting the resulting error conditions
Creating Unity Catalog views with CREATE VIEW that join tables and apply filters
Using Liquid Clustering for data lifecycle and query performance management
Comparing Liquid Clustering benefits against traditional Hive-style partitioning
Assuming CDF can be enabled on any existing table without checking table properties or supported operations first.
Forgetting that a view's accessibility depends on Unity Catalog schema grants and the definer's privileges, not just the SELECT.
Treating Liquid Clustering as a drop-in replacement for partitioning without accounting for when each layout actually helps.
Click any question to see the full explanation and answer options, or start a focused practice session above.
A data analyst needs to optimize query performance for a large sales table that is frequently filtered by 'region_id'. Which physical data modeling strategy should be implemented to minimize data scanning?
2Refer to the exhibit. The 'sales_data' table is growing rapidly. You notice queries filtering by 'event_date' are fast, but queries filtering by 'id' are slow. What is the most effective data modeling change to optimize for 'id' lookups?
3When designing a star schema in Databricks SQL, why is it recommended to use Delta Lake for both Fact and Dimension tables?
4Which THREE of the following are benefits of using Liquid Clustering instead of traditional partitioning in Databricks SQL?
5You are modeling a table where users need to query based on a 'user_id' but also need to perform historical point-in-time analysis. Which feature is most appropriate?
6An organization requires that certain sensitive columns be removed from a table for specific groups of users. Which Databricks feature should be used to enforce this at the data modeling level?
7Refer to the exhibit. You are attempting to enable Change Data Feed (CDF) on an existing Delta table but receive this error. Why is this error occurring?
8What is the primary function of the 'VACUUM' command in Databricks SQL data modeling?
9When designing a table to support frequent 'MERGE' operations, which data modeling practice will lead to the best performance?
10A data analyst is designing a star schema in Databricks SQL to optimize query performance for a large sales dataset. Which strategy most effectively minimizes data shuffling during join operations between a large fact table and a small dimension table?
11An analyst needs to manage data lifecycle and performance in Databricks SQL. Which TWO of the following tasks are best achieved using the Liquid Clustering feature?
12Refer to the exhibit. An analyst is troubleshooting a performance issue where frequent small inserts into a Delta table result in degraded query performance over time. The exhibit shows the configuration applied. What is the expected behavior of these properties?
13An analyst is building a dimensional model in Databricks SQL and needs to create a table that stores slowly changing dimension type 2 (SCD2) history for customers. The table must track valid_from and valid_to timestamps and a current flag. Which table type in Databricks SQL is best suited for this purpose?
14A data analyst is designing a dimension table in Databricks SQL that will be used in a star schema. The table contains a natural business key (e.g., product_code) and a surrogate key (e.g., product_sk). The analyst wants to ensure that the surrogate key is unique and automatically generated for each new row, while also enforcing that the natural key is unique. Which approach best achieves these requirements?
15A data analyst is creating a view in Databricks SQL that joins a fact table with several dimension tables. The analyst wants to ensure that the view always returns the latest data and that any changes to the underlying tables are immediately reflected. Which type of view should the analyst create?
16A data analyst is designing a Delta table in Databricks SQL to store clickstream events. The table will be queried primarily by filtering on event_date and then by user_id. The analyst wants to optimize data skipping for both columns without over-partitioning. Which approach should the analyst use?
17A data analyst is designing a table to store customer orders. The table will be frequently queried by order_date and customer_id. The analyst wants to optimize query performance for these filters. Which physical data modeling technique should the analyst use?
18An analyst is designing a Delta table in Databricks SQL that will store customer transactions. The table must enforce that the 'transaction_amount' is always positive and that 'customer_id' is not null. The analyst wants to ensure that any future inserts or updates that violate these rules are rejected. Which approach should the analyst use?
19A data analyst is working with a Delta table that contains a column 'status' with values 'active', 'inactive', and 'pending'. The analyst wants to enforce that only these three values can be inserted or updated. Which Databricks SQL feature should the analyst use?
20A data analyst is creating a table in Databricks SQL to store product information. The analyst wants to ensure that the table is automatically optimized for query performance as data is added, without manual intervention. Which table type should the analyst use?
21A data analyst needs to create a view in Databricks SQL that combines data from two tables and applies a filter. The view should be accessible to other users in the same Unity Catalog schema. Which SQL statement should the analyst use?
22A data analyst is working with a Delta table that contains a column 'sensitive_info' which should be redacted for users in the 'marketing' group. The analyst wants to ensure that users in that group see a masked value while other users see the actual data. Which Databricks feature should the analyst use?
23A data analyst is modeling a Delta table in Databricks SQL that stores product inventory snapshots. The table has columns snapshot_date (DATE), product_id (STRING), warehouse_id (STRING), and quantity_on_hand (INT). Queries frequently filter on snapshot_date and then join to a product dimension. The analyst wants to minimize the amount of data scanned for date-filtered queries while keeping the table simple and avoiding manual file management. Which approach best achieves this in Databricks SQL?
Write correct Databricks SQL DDL for tables and views, enable and reason about Change Data Feed, and choose between Liquid Clustering and partitioning. The key is knowing which layout or feature matches the stated performance, lifecycle, and access requirement.
The Courseiva Databricks-DA-Assoc question bank contains 23 questions in the Data Modeling with Databricks SQL domain. Click any question to see the full explanation and answer breakdown.
Start with a 10-question focused session to identify your baseline accuracy in this domain. Read every explanation — even for questions you answer correctly — to understand the reasoning. Once you score consistently above 80%, move to a 20–30 question session to confirm depth before moving to the next domain.
Yes — the session launcher on this page draws questions exclusively from the Data Modeling with Databricks SQL domain. Choose 10, 20, 30, or 50 questions for a focused session, or click individual questions to review them one by one.
Save your results, see per-domain analytics, and get readiness scores — free, for every certification.
Sign Up FreeFree forever · Every certification included