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

"Databricks Certified Data Analyst Associate Exam", also known as Databricks-Certified-Data-Analyst-Associate exam, is a Databricks Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 118 Q&As to your Databricks-Certified-Data-Analyst-Associate Exam preparation. In the Databricks-Certified-Data-Analyst-Associate exam resources, you will cover every field and category in Data Analyst Certification helping to ready you for your successful Databricks Certification.

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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
  • Updated: Nov 07, 2026
  • No. of Questions: 118 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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

TopicDetails
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
  • 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
  • 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 4
  • 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 5
  • 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.

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

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Databricks Certified Data Analyst Associate Sample Questions:

A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

  • A. They will need to create two separate dashboards.
  • B. They will need to alter the Query to return two separate sets of results.
  • C. They will need to add two separate visualizations to the dashboard based on the same Query.
  • D. They will need to decide on a single data visualization to add to the dashboard.
  • E. They will need to copy the Query and create one data visualization per query.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

  • A. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
  • B. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
  • C. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
  • D. All files with the .customers extension are deleted.
  • E. Running SELECT * FROM accounts.customers will return all rows in the table.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Consider the following two statements:
Statement 1:

Statement 2:
Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

  • A. When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
  • B. There is no difference between the result sets for both statements.
  • C. Both statements will fail because Databricks SQL does not support those join types.
  • D. When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
  • E. The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

  • A. Last-mile
  • B. Last-mile ETL
  • C. Data blending
  • D. Data testing
  • E. Data enhancement
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).

A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

  • A. They can set up an Alert with one-time notifications.
  • B. They can set up an Alert with a new webhook alert destination.
  • C. They can set up an Alert with a custom template.
  • D. They can set up an Alert with a new email alert destination.
  • E. They can set up an Alert without notifications.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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