Databricks Databricks-Certified-Data-Analyst-Associate Exam dumps [2026]

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Number:	Databricks-Certified-Data-Analyst-Associate
Exam Duration:	90 minutes
Exam Price:	$200 USD
Passing Score:	Approximately 70% (not officially disclosed)
Real Exam Qty:	45
Certificate Validity Period:	2 years
Exam Format:	Multiple-choice, Scenario-based questions
Available Languages:	English
Recommended Training:	Databricks Academy - Data Analyst Associate Learning Path
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored or authorized test center
Pre Condition:	No mandatory prerequisites; recommended 6+ months hands-on experience in data analysis and SQL usage with Databricks
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-analyst-associate

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.
Topic 5	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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
C. The suppliers table now contains only the data from the new suppliers table.
D. The command fails because it is written incorrectly.
E. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.

Answer: D

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
* INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [,
...] ) | BY NAME ] query
* INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query
The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted.
The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 113
A managed table and an unmanaged external table were both created in Databricks SQL, and data was ingested into each table. Later, both tables were dropped.
What is the status of data for each of those tables?

A. The data in both tables were deleted.
B. The data in the managed table were deleted, and the data in the unmanaged external table were left untouched.
C. The data in the unmanaged external table were deleted, and the data in the managed table were left untouched.
D. The data in both tables were left untouched.

Answer: B

Explanation:
Option B is correct. In Databricks, when a managed table is dropped, Databricks removes the table and its associated managed storage location. For an external table, Databricks removes only the table metadata; the underlying external data files are not deleted. Therefore, dropping both tables deletes the managed table's data but leaves the external table's data untouched. Official Databricks documentation states that DROP TABLE
"deletes the table and removes the directory associated with the table from the file system if the table is not an EXTERNAL table," and for external tables, Unity Catalog "removes the table metadata but does not delete the underlying data files."

NEW QUESTION # 114
Which of the following statements about a refresh schedule is incorrect?

A. A query can be refreshed anywhere from 1 minute lo 2 weeks
B. You must have workspace administrator privileges to configure a refresh schedule
C. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
D. A refresh schedule is not the same as an alert.
E. Refresh schedules can be configured in the Query Editor.

Answer: B

Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.

NEW QUESTION # 115
Which statement describes descriptive statistics?

A. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
B. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
C. A branch of statistics that uses summary statistics to categorically describe and summarize data.
D. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.

Answer: B

Explanation:
Descriptive statistics refer to statistical methods used to describe and summarize the basic features of data in a study. They provide simple summaries about the sample and the measures, often including metrics such as mean, median, mode, range, and standard deviation. Databricks learning materials highlight that descriptive statistics use summary statistics to quantitatively describe and summarize data, providing insight into data distributions without making inferences or predictions.

NEW QUESTION # 116
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain a less refined view of data than Silver tables.
B. Gold tables are more likely to contain more data than Silver tables.
C. Gold tables are more likely to contain valuable data than Silver tables.
D. Gold tables are more likely to contain truthful data than Silver tables.
E. Gold tables are more likely to contain aggregations than Silver tables.

Answer: E

Explanation:
Option A is correct. Silver tables are cleaned, validated, and enriched versions of data, often still retaining detailed records. Gold tables are typically business-ready, analytics-focused, and more likely to contain aggregations, dimensional models, and reporting-ready metrics. Official Databricks extract: Silver is associated with "Data cleaning and validation," while Gold is associated with "Dimensional modeling and aggregation." Databricks also states that Gold data is often highly aggregated and tailored for analytics and reporting.

NEW QUESTION # 117
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