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

  • Exam Code: Databricks-Certified-Data-Analyst-Associate
  • Exam Name: Databricks Certified Data Analyst Associate Exam
  • Updated: Oct 12, 2026     Q & A: 118 Questions and Answers

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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
  • 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 3
  • 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 4
  • 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 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.

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

Databricks Certified Data Analyst Associate Sample Questions:

Question #1

A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

  • A. Edit the Owner field in the table page by selecting the Admins group
  • B. Edit the Owner field in the table page by selecting the new owner ' s account
  • C. Edit the Owner field in the table page by removing all access
  • D. Edit the Owner field in the table page by removing their own account
  • E. Edit the Owner field in the table page by selecting All Users
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

Query History provides Databricks SQL users with a lot of benefits. A data analyst has been asked to share all of these benefits with their team as part of a training exercise. One of the benefit statements the analyst provided to their team is incorrect.
Which statement about Query History is incorrect?

  • A. It can be used to automate query execution on multiple warehouses (formerly endpoints).
  • B. It can be used to troubleshoot slow running queries.
  • C. It can be used to debug queries.
  • D. It can be used to view the query plan of queries that have run.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

A data analyst is working with the following table my_table:
customer_name dollars_spent
Hex Sprockets [125.34, 100.15, 9003.99]
Dented Fenders [16.99, 200.85, 33.49, 88.17]
The analyst wants to divide each value in the dollars_spent array by 100 to get the spend in terms of hundreds of dollars using the following code block:
SELECT
customer_name,
_______
FROM my_table;
Which line of code can be used to fill in the blank so that the above code block successfully completes the task?

  • A. TRANSFORM(dollars_spent, value / 100) AS hundreds_spent
  • B. TRANSFORM(dollars_spent, dollars_spent / 100) AS hundreds_spent
  • C. TRANSFORM(dollars_spent, value - > value / 100) AS hundreds_spent
  • D. TRANSFORM(hundreds_spent, dollars_spent / 100)
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

A BI analyst is building an analytical data model in Databricks using Delta Lake tables. The source system contains transactional sales data that changes frequently. The analyst chooses to apply the Data Vault 2.0 methodology to manage historical changes while ensuring scalability and auditability across multiple business domains.
Which component is used to capture the many-to-many relationship between hubs in a Data Vault v2 model?

  • A. Hub Table
  • B. Satellite Table
  • C. Reference Table
  • D. Link Table
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

  • A. Implement Apache Spark with a distributed cluster to process the data in parallel.
  • B. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
  • C. Use a local Python script with the pandas library to read and analyze the CSV file.
  • D. Deploy a real-time streaming solution using Spark Streaming to process incoming data.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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