Databricks Certified Data Analyst Associate Exam Questions 2026 – Real Practice Test with Verified Answers

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache.
Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

Explanation:
Option D is correct because the desired result needs one row per customer, so the query must aggregate orders by customer_name. It also needs the output column name number_of_orders, so the aggregate expression must be aliased. Databricks SQL documentation states that count “returns the number” of rows in a group, and the SELECT clause supports a column alias for an expression result. Therefore, count(order_id) AS number_of_orders with GROUP BY customer_name is the correct statement.
Option B counts correctly but does not alias the output column as required.
Option A does not aggregate.
Option C uses invalid SQL syntax because USE customer_name is not a grouping clause.
Reference: Databricks count aggregate function and SELECT clause documentation.

Explanation:
Databricks SQL uses SQL warehouses as its compute resource. A SQL warehouse is a dedicated compute engine designed specifically for executing SQL queries and powering dashboards within the Databricks workspace. According to Databricks official documentation, SQL warehouses are optimized for fast, scalable query execution, whereas clusters are used primarily for data engineering and machine learning workloads.

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views.
Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore)
until it is explicitly dropped.
Key points:
Views do not store data themselves but reference data from underlying tables.
Logging out or being inactive does not delete or alter views.
Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
Therefore, after logging back in―even days later―a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn’t changed).
Reference: Views - Databricks Documentation

Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data.
Reference: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL