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Databricks Databricks-Certified-Data-Analyst-Associate 시험요강:

주제	소개
주제 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.
주제 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.
주제 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.
주제 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.
주제 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.

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q105-Q110):

질문 # 105
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 Admins group
C. Edit the Owner field in the table page by removing their own account
D. Edit the Owner field in the table page by selecting the new owner's account
E. Edit the Owner field in the table page by selecting All Users

정답：D

설명：
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

질문 # 106
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

A. Separate endpoints for each section
B. Direct text written into the dashboard in editing mode
C. Separate queries for each section
D. Separate color palettes for each section
E. Markdown-based text boxes

정답：E

설명：
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks

질문 # 107
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 CUPDATE suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
B. Option AMERGE INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
C. Option BCOPY INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
D. Option DINSERT INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;

정답：B

설명：
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 *.

질문 # 108
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 Admins group
C. Edit the Owner field in the table page by removing their own account
D. Edit the Owner field in the table page by selecting the new owner's account
E. Edit the Owner field in the table page by selecting All Users

정답：D

설명：
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

질문 # 109
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 deleted.
B. The data in both tables 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 the managed table were deleted, and the data in the unmanaged external table were left untouched.

정답：D

설명：
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."

질문 # 110
......

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