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Databricks Certified Data Analyst Associate Exam Questions
- Topic 1: Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints/warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint/warehouses, trade-off between cluster size and cost for Databricks SQL endpoints/warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
- Topic 2: Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
- Topic 3: SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
- Topic 4: Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
- Topic 5: Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
Free Databricks Databricks Certified Data Analyst Associate Exam Actual Questions
Note: Premium Questions for Databricks Certified Data Analyst Associate were last updated On Nov. 02, 2026 (see below)
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
A Data Analyst is working on sensor_df; this DataFrame contains two columns: record_datetime timestamp and record array.
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A data analyst has been asked to use the below table sales_table to rank products within region by the sales.
Input table:
region product sales
WEST A 1880.59
EAST A 2045.99
EAST B 4583.23
WEST B 3391.19
The result of the query should look like this:
region product rank
EAST B 1
EAST A 2
WEST B 1
WEST A 2
Which query will accomplish this task?
A.
SELECT
region,
product,
RANK() OVER (
ORDER BY sales DESC
) AS rank
FROM sales_table;
B.
SELECT
region,
product,
RANK() OVER (
PARTITION BY product
ORDER BY sales DESC
) AS rank
FROM sales_table;
C.
SELECT
region,
product,
RANK() OVER (
PARTITION BY region
) AS rank
FROM sales_table;
D.
SELECT
region,
product,
RANK() OVER (
PARTITION BY region
ORDER BY sales DESC
) AS rank
FROM sales_table;
A data analyst filters rows where the tags array includes the value 'sql' using this query:
SELECT * FROM main.analytics.articles WHERE tags = 'sql';
This query returns no results.
How should the analyst query to filter for rows where the tags array contains 'sql'?
Which open-source project helps to enable the data lakehouse by adding organization, reliability, performance, and data governance to data lake architectures?
The correct answer is C because Delta Lake is the open-source storage layer that provides reliability and structure for data lakes. Delta Lake brings ACID transactions, scalable metadata handling, and batch/streaming support to data lake storage, which are core capabilities behind the lakehouse architecture. MLflow is for machine learning lifecycle management, Apache Spark is a distributed processing engine, and Databricks SQL is a Databricks product for SQL analytics, not the open-source storage project described.
Official documentation extract used: Databricks states that Delta Lake is ''open source software'' and provides ''ACID transactions and scalable metadata handling.''
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