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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
Certificate Validity Period:	2 years
Exam Format:	Multiple Choice, Multiple Select
Exam Price:	$200 USD
Passing Score:	70%
Related Certifications:	Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate
Real Exam Qty:	45–60
Available Languages:	English
Exam Duration:	90 minutes
Recommended Training:	Databricks SQL Training Courses Databricks Academy - Data Analyst Learning Path
Exam Registration:	Databricks Certification Portal Kryterion Webassessor Registration
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

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

Topic	Details
Topic 1	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 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	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	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	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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be applied at scale to array data objects
B. When custom logic needs to be applied to simple, unnested data
C. When built-in functions need to run through the Catalyst Optimizer
D. When custom logic needs to be converted to Python-native code
E. When built-in functions are taking too long to perform tasks

Answer: A

Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

NEW QUESTION # 90
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 a custom template.
B. They can set up an Alert without notifications.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with one-time notifications.
E. They can set up an Alert with a new email alert destination.

Answer: C

Explanation:
Option D is correct. Databricks SQL alerts run a query on a schedule, evaluate a condition, and notify destinations when the condition is met. Since the requirement is notification through a messaging webhook, the alert should use a webhook alert destination. Official Databricks extract: alerts "run queries on a schedule and notify you when a condition that you define is met." Databricks notification documentation also states that admins can configure alternate notification destinations using webhooks.

NEW QUESTION # 91
A data analyst filters rows where the tags array includes the value ' sql ' using this query:
SELECT *
FROM main.analytics.articles
WHERE tags = ' sql ' ;
This query returns no results.
How should the analyst query to filter for rows where the tags array contains ' sql ' ?

A. SELECT * FROM main.analytics.articles WHERE array_contains(tags, ' sql ' );
B. SELECT * FROM main.analytics.articles WHERE ' sql ' IN tags;
C. SELECT * FROM main.analytics.articles WHERE tags LIKE ' %sql% ' ;
D. SELECT * FROM main.analytics.articles WHERE tags IN ( ' sql ' );

Answer: A

Explanation:
Option D is correct. The column tags is an array, so comparing the entire array to the scalar string ' sql ' is incorrect. The correct Databricks SQL function is array_contains(array, value), which returns true when the array contains the specified value. The uploaded document has a spelling error, array_conatins; the corrected Databricks function name is array_contains. Official Databricks documentation states that array_contains
"returns true if array contains value."

NEW QUESTION # 92
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 DINSERT 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 BCOPY INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;

Answer: A

Explanation:
The correct answer is A because MERGE INTO is the Databricks SQL command used to combine source rows with a target table based on a match condition. The condition checks whether supplier_id already exists.
If a row does not match, WHEN NOT MATCHED THEN INSERT * inserts it. If a row already matches, no WHEN MATCHED action is provided, so the existing row remains unchanged. This prevents duplicate supplier rows.
Official documentation extract used: Databricks shows the pattern MERGE INTO target USING source ON target.key = source.key WHEN NOT MATCHED THEN INSERT *.

NEW QUESTION # 93
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. Query Editor # Y Axis
B. Dashboard Editor # Axes # Y Axis
C. Settings # User Settings # Scaling
D. Visualization Editor # Y Axis

Answer: D

Explanation:
The correct answer is C because axis settings are configured in the visualization editor, specifically under the Y-axis settings. The Query Editor is for writing SQL, not configuring chart axes. User Settings do not control individual chart scales. Dashboard editing may contain visualizations, but the specific control is in the visualization editor's Y Axis configuration.
Official documentation extract used: Databricks chart options state that to configure formatting for the X axis or Y axis, click the corresponding axis tab, and the Y-axis supports scale and range controls.

NEW QUESTION # 94
......

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