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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 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 4	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 5	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.

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q17-Q22):

질문 # 17
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 Dashboard Editor
B. The Query Editor
C. Data bxplorer
D. The Visualization editor

정답：B

설명：
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.
Reference: Schedule a query - Databricks Documentation

질문 # 18
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
B. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
C. All data visualizations and counters must be created using Queries.
D. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.

정답：C

설명：
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.

질문 # 19
Which of the following data workloads will utilize a Gold table as its source?

A. A job that cleans data by removing malformatted records.
B. A job that queries aggregated data designed to feed into a dashboard.
C. A job that ingests raw data from a streaming source into the Lakehouse.
D. A job that enriches data by parsing its timestamps into a human-readable format.
E. A job that aggregates uncleaned data to create standard summary statistics.

정답：B

설명：
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

질문 # 20
Which example of data projects represents a common analytics application to be completed in Databricks SQL?

A. Predicting customer churn by automatically developing time-series models
B. Testing the latency of real-time streaming workloads
C. Performing analysis-specific ETL on gold-layer tables
D. Monitoring the performance of deployed machine learning solutions

정답：C

설명：
Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a "last-mile" analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.

질문 # 21
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 add two separate visualizations to the dashboard based on the same Query.
C. They will need to alter the Query to return two separate sets of results.
D. They will need to create two separate dashboards.
E. They will need to copy the Query and create one data visualization per query.

정답：B

설명：
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

질문 # 22
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