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

주제	소개
주제 1	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 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.
주제 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.
주제 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.
주제 5	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.

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q28-Q33):

질문 # 28
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 columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D. All records from all columns will be displayed with the values in user_ltv.
E. All age values less than 18 will be returned as null values, all other columns will be returned with the values in user_ltv.

정답：B

설명：
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.

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 central tendency of a set of values.
D. Variance is a measure of how far a set of values is spread out from the sets central value.

정답：D

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

질문 # 30
Data engineers and data analysts are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline can have different notebook sources in SQL and Python.
B. The pipeline will need to use a batch source in place of a streaming source.
C. The pipeline will need to be written entirely in Python.
D. The pipeline will need to be written entirely in SQL.

정답：A

설명：
Option A is correct. Delta Live Tables, now documented under Lakeflow Declarative Pipelines, supports both Python and SQL source files in the same pipeline. The pipeline does not need to be rewritten entirely in one language, and it can still use streaming inputs. Official Databricks extract: "You can add Python and SQL source files to the same pipeline." The same documentation also shows examples for batch and streaming reads in Python and SQL.

질문 # 31
Which of the following commands will return the location of database customer360?

A. USE DATABASE customer360;
B. DESCRIBE LOCATION customer360;
C. DESCRIBE DATABASE customer360;
D. ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' );
E. DROP DATABASE customer360;

정답：C

설명：
Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema's "location on the filesystem."

질문 # 32
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

A. ( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))
B. Option B
C. Option D
D. Option E
E. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
F. ( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
G. ( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
H. Option C
I. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
J. Option A

정답：A

설명：
Option E is correct. A Silver-to-Gold hop typically reads cleaned/refined Silver data and writes aggregated, analytics-ready Gold data. The query reads from sales, groups by store, and aggregates sum( " sales " ), producing a summary table suitable for reporting or dashboarding. That matches the Gold layer. Option A reads from a raw location, which is not Silver-to-Gold. Option D filters invalid units, which is a cleaning step associated with Silver. Options B and C add a derived column but do not create a Gold-level aggregated table.
Official Databricks medallion architecture documentation states that Silver is where data cleanup and validation are performed, while the Gold layer "consists of aggregated data tailored for analytics and reporting." Databricks Structured Streaming documentation also shows .writeStream.outputMode( " complete
" ).toTable(...) as a valid output mode pattern for stateful streaming aggregations.

질문 # 33
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