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

주제	소개
주제 1	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 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	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 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 Databricks-Certified-Data-Analyst-Associate 덤프구매전 한국어 온라인상담서비스부터 구매후 덤프 무료 업데이트버전제공 , Databricks Databricks-Certified-Data-Analyst-Associate시험불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. Databricks Databricks-Certified-Data-Analyst-Associate 덤프는 인기덤프인데 지금까지 덤프를 구매한후 환불신청하신 분은 아직 없었습니다.

최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q66-Q71):

질문 # 66
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 the managed table were deleted, and the data in the unmanaged external table were left untouched.
B. The data in both tables were left untouched.
C. The data in both tables were deleted.
D. The data in the unmanaged external table were deleted, and the data in the managed table were left untouched.

정답：A

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 # 67
What does Partner Connect do when connecting Power Bl and Tableau?

A. Downloads and installs an ODBC driver.
B. Downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).
C. Creates a Personal Access Token for authentication into Databricks SQL and emails it to you.
D. Creates a Personal Access Token. downloads and installs an ODBC driver, and downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).

정답：D

설명：
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:
* Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.
* ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.
* Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.
Reference: Connect Tableau and Databricks

질문 # 68
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

A. Advanced dashboarding capabilities
B. Computations, data, and analytical tools on the same platform
C. Automated alerting systems
D. Simultaneous multi-user support

정답：B

설명：
Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.

질문 # 69
Which statement describes descriptive statistics?

A. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
B. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
C. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
D. A branch of statistics that uses summary statistics to categorically describe and summarize data.

정답：B

질문 # 70
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?

A. Data engineer
B. Platform architect
C. Data scientist
D. Business analyst

정답：D

설명：
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes.
Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform

질문 # 71
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