Databricks-Certified-Data-Analyst-Associate Exam Topics

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

Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Code:	Databricks-Certified-Data-Analyst-Associate
Certifications:	Databricks Certified Data Analyst Certification
Actual Exam Duration:	90 minutes
Expected no. of Questions in Actual Exam:	45
Exam Registration Price:	$200
See Expected Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Expected Questions in Actual Exam

Databricks-Certified-Data-Analyst-Associate Exam Objectives :

Section	Weight	Objectives
Section 1: Databricks SQL	22%	Describe the key audience and side audiences for Databricks SQL. Describe that a variety of users can view and run Databricks SQL dashboards as stakeholders. Describe the benefits of using Databricks SQL for in-Lakehouse platform data processing. Describe how to complete a basic Databricks SQL query. Identify Databricks SQL queries as a place to write and run SQL code. Identify the information displayed in the schema browser from the Query Editor page. Identify Databricks SQL dashboards as a place to display the results of multiple queries at once. Describe how to complete a basic Databricks SQL dashboard. Describe how dashboards can be configured to automatically refresh. Describe the purpose of Databricks SQL endpoints/warehouses. Identify Serverless Databricks SQL endpoint/warehouses as a quick-starting option. Describe the trade-off between cluster size and cost for Databricks SQL endpoints/warehouses. Identify Partner Connect as a tool for implementing simple integrations with a number of other data products. Describe how to connect Databricks SQL to ingestion tools like Fivetran. Identify the need to be set up with a partner to use it for Partner Connect. Identify small-file upload as a solution for importing small text files like lookup tables and quick data integrations. Import from object storage using Databricks SQL. Identify that Databricks SQL can ingest directories of files of the files are the same type. Describe how to connect Databricks SQL to visualization tools like Tableau, Power BI, and Looker. Identify Databricks SQL as a complementary tool for BI partner tool workflows. Describe the medallion architecture as a sequential data organization and pipeline system of progressively cleaner data. Identify the gold layer as the most common layer for data analysts using Databricks SQL. Describe the cautions and benefits of working with streaming data. Identify that the Lakehouse allows the mixing of batch and streaming workloads.
Section 2: Data Management	20%	Describe Delta Lake as a tool for managing data files. Describe that Delta Lake manages table metadata. Identify that Delta Lake tables maintain history for a period of time. Describe the benefits of Delta Lake within the Lakehouse. Describe persistence and scope of tables on Databricks. Compare and contrast the behavior of managed and unmanaged tables. Identify whether a table is managed or unmanaged. Explain how the LOCATION keyword changes the default location of database contents. Use Databricks to create, use, and drop databases, tables, and views. Describe the persistence of data in a view and a temp view Compare and contrast views and temp views. Explore, preview, and secure data using Data Explorer. Use Databricks to create, drop, and rename tables. Identify the table owner using Data Explorer. Change access rights to a table using Data Explorer. Describe the responsibilities of a table owner. Identify organization-specific considerations of PII data
Section 3: SQL in the Lakehouse	29%	Identify a query that retrieves data from the database with specific conditions Identify the output of a SELECT query Compare and contrast MERGE INTO, INSERT TABLE, and COPY INTO. Simplify queries using subqueries. Compare and contrast different types of JOINs. Aggregate data to achieve a desired output. Manage nested data formats and sources within tables. Use cube and roll-up to aggregate a data table. Compare and contrast roll-up and cube. Use windowing to aggregate time data. Identify a benefit of having ANSI SQL as the standard in the Lakehouse. Identify, access, and clean silver-level data. Utilize query history and caching to reduce development time and query latency. Optimize performance using higher-order Spark SQL functions. Create and apply UDFs in common scaling scenarios.
Section 4: Data Visualization and Dashboarding	18%	Create basic, schema-specific visualizations using Databricks SQL. Identify which types of visualizations can be developed in Databricks SQL (table, details, counter, pivot). Explain how visualization formatting changes the reception of a visualization Describe how to add visual appeal through formatting Identify that customizable tables can be used as visualizations within Databricks SQL. Describe how different visualizations tell different stories. Create customized data visualizations to aid in data storytelling. Create a dashboard using multiple existing visualizations from Databricks SQL Queries. Describe how to change the colors of all of the visualizations in a dashboard. Describe how query parameters change the output of underlying queries within a dashboard Identify the behavior of a dashboard parameter Identify the use of the "Query Based Dropdown List" as a way to create a query parameter from the distinct output of a different query. Identify the method for sharing a dashboard with up-to-date results. Describe the pros and cons of sharing dashboards in different ways Identify that users without permission to all queries, databases, and endpoints can easily refresh a dashboard using the owner's credentials. Describe how to configure a refresh schedule Identify what happens if a refresh rate is less than the Warehouse's "Auto Stop" Describe how to configure and troubleshoot a basic alert Describe how notifications are sent when alerts are set up based on the configuration
Section 5: Analytics applications	11%	Compare and contrast discrete and continuous statistics. Describe descriptive statistics. Describe key moments of statistical distributions. Compare and contrast key statistical measures. Describe data enhancement as a common analytics application. Enhance data in a common analytics application. Identify a scenario in which data enhancement would be beneficial. Describe the blending of data between two source applications. Identify a scenario in which data blending would be beneficial. Perform last-mile ETL as project-specific data enhancement.
Official Information		https://www.databricks.com/learn/certification/data-analyst-associate

Updates in the Databricks-Certified-Data-Analyst-Associate Exam Topics:

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