Quiz 2026 Efficient Databricks Databricks-Certified-Data-Analyst-Associate: Latest Databricks Certified Data Analyst Associate Exam Exam Notes

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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
Passing Score:	70%
Real Exam Qty:	45–60
Related Certifications:	Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate
Available Languages:	English
Exam Price:	$200 USD
Exam Duration:	90 minutes
Certificate Validity Period:	2 years
Exam Format:	Multiple Choice, Multiple Select
Recommended Training:	Databricks Academy - Data Analyst Learning Path Databricks SQL Training Courses
Exam Registration:	Kryterion Webassessor Registration Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored exam via remote monitoring (Kryterion Webassessor platform)
Pre Condition:	No formal prerequisites required, but familiarity with SQL and basic data analysis concepts is recommended.
Official Syllabus URL:	https://www.databricks.com/learn/certification

>> Latest Databricks-Certified-Data-Analyst-Associate Exam Notes <<

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

Topic	Details
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 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	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	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 (Q115-Q120):

NEW QUESTION # 115
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 add two separate visualizations to the dashboard based on the same Query.
B. They will need to create two separate dashboards.
C. They will need to alter the Query to return two separate sets of results.
D. They will need to copy the Query and create one data visualization per query.
E. They will need to decide on a single data visualization to add to the dashboard.

Answer: A

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 116
What is used as a compute resource for Databricks SQL?

A. Downstream BI tools integrated with Databricks SQL
B. SQL warehouses
C. Standard clusters
D. Single-node clusters

Answer: B

Explanation:
Databricks SQL uses SQL warehouses as its compute resource. A SQL warehouse is a dedicated compute engine designed specifically for executing SQL queries and powering dashboards within the Databricks workspace. According to Databricks official documentation, SQL warehouses are optimized for fast, scalable query execution, whereas clusters are used primarily for data engineering and machine learning workloads.

NEW QUESTION # 117
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?

A. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()
B. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()
C. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
D. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()

Answer: B

Explanation:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.

NEW QUESTION # 118
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Business analyst
B. Business intelligence analyst
C. Data engineer
D. SQL analyst
E. Data analyst

Answer: C

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 119
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. Uses withColumn, but selects sensor_id, status, and health as if they are already top-level columns.
B. exploded_df = sensor_df.withColumn( " record_exploded " , explode( " record " ))exploded_df = exploded_df.select( " record_datetime " , " record_exploded.sensor_id " , " record_exploded.status " , " record_exploded.health " )
C. Selects nested fields before correctly creating the exploded column.
D. exploded_df = exploded_df.select( " record_datetime " , " record_exploded " )

Answer: B

Explanation:
Option C is correct after correcting the formatting and typing errors in the uploaded option text. The analyst needs explode( " record " ) because the record column is an array, and the requirement is to return one array item per row. Then the analyst must select fields from the exploded struct using dot notation, such as record_exploded.sensor_id, record_exploded.status, and record_exploded.health. Databricks PySpark documentation states that explode "returns a new row for each element in the given array or map," and withColumn returns a new DataFrame by adding or replacing a column. The select method projects expressions or column names into the resulting DataFrame.

NEW QUESTION # 120
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