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

A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard.Reference:Visualization in Databricks SQL,Visualize queries and create a dashboard in Databricks SQL

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

Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties.Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1.Reference:Discover and manage data using Data Explorer

A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions.Reference:

Delta Lake: Lakehouse, warehouse, advantages | Definition

Synapse -- Data Lake vs. Delta Lake vs. Data Lakehouse

Data Lake vs. Delta Lake - A Detailed Comparison

Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide