Databricks Databricks-Certified-Data-Analyst-Associate시험대비 - Databricks-Certified-Data-Analyst-Associate시험덤프공부

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

주제	소개
주제 1	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.
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	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 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.

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Databricks-Certified-Data-Analyst-Associate시험덤프공부, Databricks-Certified-Data-Analyst-Associate인기덤프문제

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q26-Q31):

질문 # 26
A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:

The result of the query should look like this:

Which of the following queries will accomplish this task?
A)

B)

C)

D)

A. Option B
B. Option D
C. Option C
D. Option A

정답：A

설명：
The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
* A. Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
* C. Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function.
The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
* D. Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function.
The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). References:
* 1: PERCENT_RANK (Transact-SQL)
* 2: Window functions in Databricks SQL
* 3: Databricks Certified Data Analyst Associate Exam Guide

질문 # 27
A data analyst needs to create an empty managed table table_name in database database_name with a specific schema. The table needs to be recreated and empty, regardless of whether or not the table already exists.
Which command can the analyst use to complete the task?

A. CREATE OR REPLACE TABLE table_name FROM database_name USING (width INT, length INT, height INT);
B. CREATE TABLE database_name.table_name USING (width INT, length INT, height INT);
C. CREATE OR REPLACE TABLE database_name.table_name USING (width INT, length INT, height INT);
D. CREATE OR REPLACE TABLE database_name.table_name (width INT, length INT, height INT);

정답：D

설명：
The correct answer is C because the task requires creating or replacing a managed table with an explicitly defined schema. In Databricks SQL, column definitions are placed directly after the table name inside parentheses. The USING clause is for specifying a data source format, not for defining columns. Option C correctly uses CREATE OR REPLACE TABLE database_name.table_name (...), which recreates the table if it already exists and creates an empty table with the specified schema.
Official documentation extract used: Databricks CREATE TABLE syntax supports [CREATE OR] REPLACE followed by the table name and a table_specification containing column identifiers and column types.

질문 # 28
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A. View
B. Spark SQL Table
C. Temporary view
D. Database
E. Delta Table

정답：C

설명：
Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a "virtual table that has no physical data," and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

질문 # 29
What describes the variance of a set of values?

A. Variance is a measure of how far a single observed value is from a set ot va IN
B. Variance is a measure of how far an observed value is from the variable ' s maximum or minimum value.
C. Variance is a measure of how far a set of values is spread out from the sets central value.
D. Variance is a measure of central tendency of a set of values.

정답：C

설명：
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1 Reference: Variance - Wikipedia

질문 # 30
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 notify their supervisor to ensure the data is handled following organizational and legal best practices.
B. Stop working with the data and delete the table and any existing metadata or data files.
C. Stop working with the data and proceed with the project using other data.
D. Drop the column containing PII data and continue with the project without notifying anybody.

정답：A

설명：
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.

질문 # 31
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