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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 Price:	$200 USD
Real Exam Qty:	45
Passing Score:	Approximately 70% (not officially disclosed)
Exam Format:	Scenario-based questions, Multiple-choice
Certificate Validity Period:	2 years
Available Languages:	English
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	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 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	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 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 (Q58-Q63):

NEW QUESTION # 58
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

A. The Query Editor
B. Data bxplorer
C. The Visualization editor
D. The Dashboard Editor

Answer: A

Explanation:
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up-to-date query results without manual intervention.

NEW QUESTION # 59
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
B. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
C. Use Delta Live Tables to establish a cluster for Fivetran to interact with
D. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
E. Use Workflows to establish a cluster for Fivetran to interact with

Answer: D

Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

NEW QUESTION # 60
A data analyst has come across a column in a table that contains personally identifiable information (PII). The data analyst should not have access to this type of PII data.
How should the data analyst proceed?

A. Stop working with the data and delete the table and any existing metadata or data files.
B. Drop the column containing PII data and continue with the project without notifying anybody.
C. Stop working with the data and proceed with the project using other data.
D. Stop working with the data and notify their supervisor to ensure the data is handled following organizational and legal best practices.

Answer: D

Explanation:
Option C is correct. If an analyst encounters PII that they should not access, they should stop using that data and escalate through the appropriate organizational process. They should not delete the table, drop the column, or silently continue. Databricks security and privacy guidance emphasizes least-privilege access, data classification by sensitivity, access control, and meeting compliance/privacy requirements, including redaction or obfuscation of PII when required. Therefore, notifying a supervisor or data owner is the correct professional and governance-aligned response. References: Databricks security, compliance, and privacy documentation.

NEW QUESTION # 61
A data analyst is using Databricks Unity Catalog. The datasets are tagged by sensitivity, and confidential data is marked with the tag key confidential. The data analyst needs to quickly find all tables tagged as confidential to review their access permissions in the Databricks workspace search bar.
Which search key text should the data analyst use to find these tables?

A. tag:confidential
B. tag=confidential
C. confidential:true
D. search tag = ' confidential '

Answer: A

Explanation:
The correct answer is C because the tag key is confidential, and Databricks workspace search uses the syntax tag: < tag_key > to search by tag key. Therefore, the correct search text is tag:confidential. Option A uses a Boolean-style syntax that is not the documented Databricks tag search syntax. Option B uses an equals sign, and Option D is not valid workspace search syntax.
Official documentation extract used: Databricks states that to search by tag key alone, use the syntax tag: < tag_key > .

NEW QUESTION # 62
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
B. They can reduce the cluster size of the SQL endpoint.
C. They can set up the dashboard's SQL endpoint to be serverless.
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: D

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 63
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