Exam Code: Databricks-Certified-Data-Analyst-Associate
Exam Name: Databricks Certified Data Analyst Associate Exam (Databricks-Certified-Data-Analyst-Associate日本語版)
Certification Provider: Databricks
Corresponding Certification: Data Analyst

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Databricks Databricks-Certified-Data-Analyst-Associate日本語 Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Number:	Databricks-Certified-Data-Analyst-Associate
Available Languages:	English
Exam Price:	$200 USD
Passing Score:	70%
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	45–60
Related Certifications:	Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate
Exam Duration:	90 minutes
Certificate Validity Period:	2 years
Recommended Training:	Databricks Academy - Data Analyst Learning Path Databricks SQL Training Courses
Exam Registration:	Databricks Certification Portal Kryterion Webassessor Registration
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate日本語 Sample Questions
Exam Way:	Online proctored exam via remote monitoring (Kryterion Webassessor platform)
Pre Condition:	No formal prerequisites required, but familiarity with SQL and basic data analysis concepts is recommended.
Official Syllabus URL:	https://www.databricks.com/learn/certification

Databricks Databricks-Certified-Data-Analyst-Associate日本語 Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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.
Topic 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.
Topic 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.
Topic 5	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.

Reference: https://www.databricks.com/learn/certification/data-analyst-associate

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