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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
Exam Duration:	90 minutes
Exam Format:	Scenario-based questions, Multiple-choice
Available Languages:	English
Passing Score:	Approximately 70% (not officially disclosed)
Real Exam Qty:	45
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Recommended Training:	Databricks Academy - Data Analyst Associate Learning Path
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored or authorized test center
Pre Condition:	No mandatory prerequisites; recommended 6+ months hands-on experience in data analysis and SQL usage with Databricks
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-analyst-associate

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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	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 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	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 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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
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 tags IN ( ' 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 array_contains(tags, ' sql ' );

Answer: D

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 # 51
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?

A. Platform architect
B. Data engineer
C. Business analyst
D. Data scientist

Answer: C

Explanation:
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes.
Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform

NEW QUESTION # 52
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A. Ability to scale workloads
B. Simplified governance
C. Ability to scale storage
D. Cloud-specific integrations
E. Avoiding vendor lock-in

Answer: E

Explanation:
Option E is correct. A major benefit of open source technologies and open data formats is avoiding vendor lock-in. Databricks supports open formats and interfaces so data can be used across tools and systems rather than being locked into a proprietary platform. Cloud integrations, governance, and workload scalability are Databricks benefits, but the specific benefit of embracing open source is avoiding vendor lock-in. Official Databricks extract: "Using open data formats and interfaces helps to avoid" vendor lock-in, and Databricks states that no proprietary data formats are used because Delta Lake and Iceberg are open source.

NEW QUESTION # 53
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 wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
B. They could submit a feature request with Databricks to add this functionality.
C. They could only run the entire program on Sundays.
D. They could redesign the data model to separate the data used in the final query into a new table.
E. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.

Answer: A

Explanation:
Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured "to run it on a time-based schedule," and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

NEW QUESTION # 54
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

A. Sankey
B. Heatmap
C. Word Cloud
D. IChoropleth
E. Pivot Table

Answer: A

Explanation:
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram

NEW QUESTION # 55
......

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