Most Recent Databricks-Certified-Data-Analyst-Associate Exam Questions & Answers

A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1.To apply a UDF to a table, the syntax isSELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDFpriceto create a new columncustomer_pricebased on the existing columnscustomer_spendandcustomer_unitsfrom the tablecustomer_summary.Reference:

What are user-defined functions (UDFs)?

User-defined scalar functions - SQL

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Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.

A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization.Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram

Delta Lake stores table data as a series of data files in a specified location, but it also stores table metadata in a transaction log. The table metadata includes the schema, partitioning information, table properties, and other configuration details. The table metadata is stored alongside the data files and is updated atomically with every write operation. The table metadata can be accessed using the DESCRIBE DETAIL command or the DeltaTable class in Scala, Python, or Java. The table metadata can also be enriched with custom tags or user-defined commit messages using the TBLPROPERTIES or userMetadata options.Reference:

Enrich Delta Lake tables with custom metadata

Delta Lake Table metadata - Stack Overflow

Metadata - The Internals of Delta Lake

Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions.Reference:Higher-order functions | Databricks on AWS,Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog,Higher-order functions - Azure Databricks | Microsoft Learn,Optimization recommendations on Databricks | Databricks on AWS