Databricks Certified Data Analyst Associate Exam Questions

Databricks Certified Data Analyst Associate Exam Exam Objectives

1. 1

   1.0 Databricks SQL (22%)

   • 1.1Describe the key audience and side audiences for Databricks SQL.
   • 1.2Describe that a variety of users can view and run Databricks SQL dashboards as stakeholders.
   • 1.3Describe the benefits of using Databricks SQL for in-Lakehouse platform data processing.
   • 1.4Describe how to complete a basic Databricks SQL query.
   • 1.5Identify Databricks SQL queries as a place to write and run SQL code.
   • 1.6Identify the information displayed in the schema browser from the Query Editor page.
   • 1.7Identify Databricks SQL dashboards as a place to display the results of multiple queries at once.
   • 1.8Describe how to complete a basic Databricks SQL dashboard.
   • 1.9Describe how dashboards can be configured to automatically refresh.
   • 1.10Describe the purpose of Databricks SQL endpoints/warehouses.
   • 1.11Identify Serverless Databricks SQL endpoint/warehouses as a quick-starting option.
   • 1.12Describe the trade-off between cluster size and cost for Databricks SQL endpoints/warehouses.
   • 1.13Identify Partner Connect as a tool for implementing simple integrations with a number of other data products.
   • 1.14Describe how to connect Databricks SQL to ingestion tools like Fivetran.
   • 1.15Identify the need to be set up with a partner to use it for Partner Connect.
   • 1.16Identify small-file upload as a solution for importing small text files like lookup tables and quick data integrations.
   • 1.17Import from object storage using Databricks SQL.
   • 1.18Identify that Databricks SQL can ingest directories of files of the files are the same type.
   • 1.19Describe how to connect Databricks SQL to visualization tools like Tableau, Power BI, and Looker.
   • 1.20Identify Databricks SQL as a complementary tool for BI partner tool workflows.
   • 1.21Describe the medallion architecture as a sequential data organization and pipeline system of progressively cleaner data.
   • 1.22Identify the gold layer as the most common layer for data analysts using Databricks SQL.
   • 1.23Describe the cautions and benefits of working with streaming data.
   • 1.24Identify that the Lakehouse allows the mixing of batch and streaming workloads.
2. 2

   2.0 Data Management (20%)

   • 2.1Describe Delta Lake as a tool for managing data files.
   • 2.2Describe that Delta Lake manages table metadata.
   • 2.3Identify that Delta Lake tables maintain history for a period of time.
   • 2.4Describe the benefits of Delta Lake within the Lakehouse.
   • 2.5Describe persistence and scope of tables on Databricks.
   • 2.6Compare and contrast the behavior of managed and unmanaged tables.
   • 2.7Identify whether a table is managed or unmanaged.
   • 2.8Explain how the LOCATION keyword changes the default location of database contents.
   • 2.9Use Databricks to create, use, and drop databases, tables, and views.
   • 2.10Describe the persistence of data in a view and a temp view
   • 2.11Compare and contrast views and temp views.
   • 2.12Explore, preview, and secure data using Data Explorer.
   • 2.13Use Databricks to create, drop, and rename tables.
   • 2.14Identify the table owner using Data Explorer.
   • 2.15Change access rights to a table using Data Explorer.
   • 2.16Describe the responsibilities of a table owner.
   • 2.17Identify organization-specific considerations of PII data
3. 3

   3.0 SQL in the Lakehouse (29%)

   • 3.1Identify a query that retrieves data from the database with specific conditions
   • 3.2Identify the output of a SELECT query
   • 3.3Compare and contrast MERGE INTO, INSERT TABLE, and COPY INTO.
   • 3.4Simplify queries using subqueries.
   • 3.5Compare and contrast different types of JOINs.
   • 3.6Aggregate data to achieve a desired output.
   • 3.7Manage nested data formats and sources within tables.
   • 3.8Use cube and roll-up to aggregate a data table.
   • 3.9Compare and contrast roll-up and cube.
   • 3.10Use windowing to aggregate time data.
   • 3.11Identify a benefit of having ANSI SQL as the standard in the Lakehouse.
   • 3.12Identify, access, and clean silver-level data.
   • 3.13Utilize query history and caching to reduce development time and query latency.
   • 3.14Optimize performance using higher-order Spark SQL functions.
   • 3.15Create and apply UDFs in common scaling scenarios.
4. 4

   4.0 Data Visualization and Dashboarding (18%)

   • 4.1Create basic, schema-specific visualizations using Databricks SQL.
   • 4.2Identify which types of visualizations can be developed in Databricks SQL (table, details, counter, pivot).
   • 4.3Explain how visualization formatting changes the reception of a visualization
   • 4.4Describe how to add visual appeal through formatting
   • 4.5Identify that customizable tables can be used as visualizations within Databricks SQL.
   • 4.6Describe how different visualizations tell different stories.
   • 4.7Create customized data visualizations to aid in data storytelling.
   • 4.8Create a dashboard using multiple existing visualizations from Databricks SQL Queries.
   • 4.9Describe how to change the colors of all of the visualizations in a dashboard.
   • 4.10Describe how query parameters change the output of underlying queries within a dashboard
   • 4.11Identify the behavior of a dashboard parameter
   • 4.12Identify the use of the "Query Based Dropdown List" as a way to create a query parameter from the distinct output of a different query.
   • 4.13Identify the method for sharing a dashboard with up-to-date results.
   • 4.14Describe the pros and cons of sharing dashboards in different ways
   • 4.15Identify that users without permission to all queries, databases, and endpoints can easily refresh a dashboard using the owner's credentials.
   • 4.16Describe how to configure a refresh schedule
   • 4.17Identify what happens if a refresh rate is less than the Warehouse's "Auto Stop"
   • 4.18Describe how to configure and troubleshoot a basic alert
   • 4.19Describe how notifications are sent when alerts are set up based on the configuration
5. 5

   5.0 Analytics applications (11%)

   • 5.1Compare and contrast discrete and continuous statistics.
   • 5.2Describe descriptive statistics.
   • 5.3Describe key moments of statistical distributions.
   • 5.4Compare and contrast key statistical measures.
   • 5.5Describe data enhancement as a common analytics application.
   • 5.6Enhance data in a common analytics application.
   • 5.7Identify a scenario in which data enhancement would be beneficial.
   • 5.8Describe the blending of data between two source applications.
   • 5.9Identify a scenario in which data blending would be beneficial.
   • 5.10Perform last-mile ETL as project-specific data enhancement.