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Exam Code: Databricks-Certified-Data-Analyst-Associate
Exam Name: Databricks Certified Data Analyst Associate Exam
Certification Provider: Databricks
Corresponding Certification: Data Analyst

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Databricks-Certified-Data-Analyst-Associate

Last Updated: Oct 15, 2026

No. of Questions: 118 Questions & Answers with Testing Engine

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

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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.

Reference: https://www.databricks.com/learn/certification/data-analyst-associate

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Databricks Certified Data Analyst Associate Sample Questions:

Question #1

Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

  • A. SUM
  • B. PIVOT
  • C. CONVERT
  • D. WHERE
  • E. TRANSFORM
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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. They could redesign the data model to separate the data used in the final query into a new table.
  • B. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
  • C. They could only run the entire program on Sundays.
  • D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
  • E. They could submit a feature request with Databricks to add this functionality.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level data. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold- level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

  • A. A refresh schedule with an interval of 10 minutes or less
  • B. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint
  • C. A refresh schedule with a Structured Streaming cluster
  • D. A refresh schedule with stakeholders included as subscribers
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

A data analyst has produced a visualization. A stakeholder has viewed the visualization and is complaining that the visualization is difficult to interpret. After looking at the visualization, the analyst determines that the scale of the y-axis must be changed.
Where are the controls for changing the scale of the y-axis in Databricks SQL?

  • A. Dashboard Editor # Axes # Y Axis
  • B. Visualization Editor # Y Axis
  • C. Settings # User Settings # Scaling
  • D. Query Editor # Y Axis
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

An analyst has been asked to combine the data in two tables: suppliers and new_suppliers. It is possible that some of the supplier_id values match in both tables, meaning those suppliers have already been added to the suppliers table. If that is the case, the data should be unchanged.
Which command will combine the two tables without duplicating the rows with the same supplier_id?

  • A. Option AMERGE INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
    supplier_idWHEN NOT MATCHED THEN INSERT *;
  • B. Option BCOPY INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
    supplier_idWHEN NOT MATCHED THEN INSERT *;
  • C. Option CUPDATE suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
    supplier_idWHEN NOT MATCHED THEN INSERT *;
  • D. Option DINSERT INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
    supplier_idWHEN NOT MATCHED THEN INSERT *;
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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