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

Databricks Certified Data Analyst Associate Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
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 within a Notebook
B. Save the SQL statement as a Query
C. Save the SQL statement in the Query History page
D. Save the SQL statement as a Dashboard

Answer: B

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 # 93
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As a complementary tool for quick in-platform Bl work
B. As an exact substitute with the same level of functionality
C. As a substitute with less functionality
D. As a complete replacement with additional functionality
E. As a complementary tool for professional-grade presentations

Answer: A

Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. References: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 94
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 copy the Query and create one data visualization per query.
C. They will need to add two separate visualizations to the dashboard based on the same Query.
D. They will need to alter the Query to return two separate sets of results.
E. They will need to create two separate dashboards.

Answer: C

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 # 95
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

A. Edit the Owner field in the table page by removing all access
B. Edit the Owner field in the table page by selecting the new owner's account
C. Edit the Owner field in the table page by selecting the Admins group
D. Edit the Owner field in the table page by removing their own account
E. Edit the Owner field in the table page by selecting All Users

Answer: B

Explanation:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A) Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B) Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D) Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E) Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table

NEW QUESTION # 96
A managed table and an unmanaged external table were both created in Databricks SQL, and data was ingested into each table. Later, both tables were dropped.
What is the status of data for each of those tables?

A. The data in both tables were left untouched.
B. The data in the managed table were deleted, and the data in the unmanaged external table were left untouched.
C. The data in the unmanaged external table were deleted, and the data in the managed table were left untouched.
D. The data in both tables were deleted.

Answer: B

Explanation:
Option B is correct. In Databricks, when a managed table is dropped, Databricks removes the table and its associated managed storage location. For an external table, Databricks removes only the table metadata; the underlying external data files are not deleted. Therefore, dropping both tables deletes the managed table's data but leaves the external table's data untouched. Official Databricks documentation states that DROP TABLE
"deletes the table and removes the directory associated with the table from the file system if the table is not an EXTERNAL table," and for external tables, Unity Catalog "removes the table metadata but does not delete the underlying data files."

NEW QUESTION # 97
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