Free Practice Questions for Databricks Databricks-Certified-Data-Engineer-Associate Exam

Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New ‘Files’ Feature in …, Databricks Repos - What it is and how we can use it | Adatis.

Databricks documents that Delta Sharing does not require data replication , but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions . Databricks specifically states that sharing within the same region incurs no egress cost , while cross-cloud or cross-region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.

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Databricks documentation for Unity Catalog lineage states that lineage is captured across queries run on Databricks , is supported for all languages , and is tracked down to the column level . It also explicitly says that lineage data includes related notebooks, jobs, and dashboards , and that the lineage can be visualized in Catalog Explorer in near real time as an interactive graph. This directly matches option D. The value of this feature is that it helps engineers understand where a table’s data came from, which upstream notebooks or jobs produced it, and which downstream assets, including dashboards, depend on it. Column-level lineage adds another layer of traceability by showing how specific fields are derived through transformations, which is especially useful for impact analysis, governance, and troubleshooting. Options A, B, and C each remove documented capabilities that Unity Catalog lineage actually supports. Therefore, the Databricks-documented answer is the full interactive graph across Delta tables and related assets, with column-level tracking included.

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JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named “age” may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader’s Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

Databricks Asset Bundles provide a modern, declarative approach to defining, testing, and deploying Databricks workflows as version-controlled code artifacts . Using YAML configuration files, engineers can describe jobs, pipelines, dependencies, and environments in a structured and reproducible way. Asset Bundles integrate well with CI/CD pipelines and external schedulers, enabling teams to package workflows, run integration tests, and deploy consistently across environments such as development, staging, and production. This aligns directly with the requirement for version control and external orchestration. The Databricks CLI (A) is primarily used as a deployment interface but does not provide the structured packaging and workflow definition capabilities of Asset Bundles. Databricks Connect (C) is intended for local development and testing of Spark code, not workflow deployment. The Databricks SDK (D) provides programmatic access to APIs but lacks the declarative, packaged deployment model required here. Databricks documentation positions Asset Bundles as the recommended solution for production-grade workflow deployment and testing within modern data engineering practices.