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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
Available Languages:	English
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Passing Score:	Approximately 70% (not officially disclosed)
Real Exam Qty:	45
Exam Format:	Multiple-choice, Scenario-based questions
Exam Duration:	90 minutes
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	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	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 (Q55-Q60):

NEW QUESTION # 55
A table named user_ltv is being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
CREATE VIEW user_ltv_no_minors AS
SELECT email, age, ltv
FROM user_ltv
WHERE
CASE
WHEN is_member( " auditing " ) THEN TRUE
ELSE age > = 18
END;
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors;
Which statement describes the results returned by this query?

A. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
B. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
D. All age values less than 18 will be returned as null values, all other columns will be returned with the values in user_ltv.
E. All records from all columns will be displayed with the values in user_ltv.

Answer: C

Explanation:
Option A is correct. The user is not a member of the auditing group, so is_member( " auditing " ) evaluates to false and the ELSE age > = 18 branch controls the filter. Rows with age > = 18 are returned; rows under 18 are omitted. "Age greater than 17" is equivalent to age > = 18 for integer ages. Official Databricks extract:
is_member() "returns TRUE if the current user is a member" of the specified group, and Databricks describes dynamic views as views that can filter rows based on group membership.

NEW QUESTION # 56
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?

A. Tooltips can be formatted.
B. Data Labels can be formatted.
C. Colors can be changed.
D. Visualization scale can be changed.
E. Borders can be added.

Answer: E

Explanation:
The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. References:
* New chart visualizations in Databricks | Databricks on AWS

NEW QUESTION # 57
A data analyst has opened the SQL Editor page and written a new SQL statement. The data analyst now wants to save that statement to easily refer back to it later and add it to a dashboard. The results of the SQL statement must be able to be displayed as a counter, table, or data visualization.
Which approach should the data analyst use to accomplish this task?

A. Save the SQL statement as a Query
B. Save the SQL statement in the Query History page
C. Save the SQL statement as a Dashboard
D. Save the SQL statement within a Notebook

Answer: A

Explanation:
The correct answer is C because Databricks SQL Editor is used to create and save SQL queries. A saved query can later be opened, reused, shared, visualized, and added to a dashboard. A dashboard is not the saved SQL statement itself; it is a reporting object that can contain query results and visualizations. Query History stores past executions, but it is not the correct object for saving a reusable SQL statement.
Official documentation extract used: Databricks states that the SQL editor can be used to "author queries" and
"create visualizations." It also states that the Save button "saves your query," and query results can be added to a dashboard.

NEW QUESTION # 58
A data analyst has written and saved a series of queries that reveal trends that need to be monitored by several stakeholders.
Which tool should the data analyst use to share the results of all of the queries to be viewed at once?

A. A dashboard
B. A Query History page
C. A data visualization tab on a Query page
D. A SQL warehouse

Answer: A

Explanation:
The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.
Official documentation extract used: Databricks states that dashboards are used to "build data visualizations and share reports with your team."

NEW QUESTION # 59
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 decide on a single data visualization to add to the dashboard.
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 add two separate visualizations to the dashboard based on the same Query.

Answer: E

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 # 60
......

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