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

The command databricks bundle deploy deploys the bundle’s local files, artifacts, and resource definitions to the configured remote Databricks workspace. Databricks documents this command as the deployment operation for Declarative Automation Bundles, and a specific target can be selected with the -t or --target option. The validate command checks whether the bundle configuration is valid but does not create or update workspace resources. The run command executes a resource that has already been deployed; it is not the deployment step. The init command creates a new bundle project from a template, while bundle deployment is not a valid command combination. Because the question asks for the single command that sends local bundle assets to the target workspace, option C is correct.

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Option C explicitly selects every required leaf field by using dot notation for nested structs. The resulting DataFrame retains event_id and event_ts while projecting device.id, device.model, device.location.latitude, and device.location.longitude as output columns. Option B expands only the immediate fields of device; the location value remains a nested struct, so the data is not fully flattened. Option A creates separate columns for the device identifier and model but retains the complete location struct instead of extracting latitude and longitude. Option D places bracket-like expressions inside column-name strings, which do not represent the required nested struct paths. Spark DataFrames support selecting nested fields through qualified column paths, making option C the direct transformation for creating analytics-friendly root-level fields.

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The OPTIMIZE command in Delta Lake is designed to compact small files into larger files to improve query performance and reduce file system overhead. Importantly, OPTIMIZE is considered an idempotent operation when run on unchanged data. This means that once files have been compacted optimally, running OPTIMIZE again does not produce additional changes unless new small files have been added to the table. Databricks ensures that file compaction is efficient and avoids unnecessary rewrites, which helps control compute costs and maintain stable performance. While features like ZORDER can be applied during OPTIMIZE to improve data skipping, re-running the same command without new data will not further reorganize files. Options A, B, and C incorrectly imply additional restructuring or changes on repeated execution. According to Delta Lake best practices, OPTIMIZE should be run periodically after data ingestion events rather than repeatedly on unchanged datasets. This behavior reinforces predictable performance and efficient resource utilization in Databricks environments.

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In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is “sqlite” because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, “sqlite” would be appropriate here. References:

Create a table using JDBC

JDBC connection string

SQLite JDBC driver

The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. References: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS

Option A is correct because Lakeflow Connect provides a built-in, fully managed Salesforce connector. It ingests Salesforce data into Databricks, supports incremental processing, and publishes governed destination tables in Unity Catalog. Managed SaaS connectors handle source-specific authentication, incremental reads, schema evolution, and automated retries, reducing the custom engineering required for API pagination, state tracking, and failure recovery. Calling the Salesforce REST API from a notebook would require the team to develop and maintain those capabilities itself. Auto Loader is designed to discover and incrementally process files arriving in cloud object storage; it does not connect directly to Salesforce. Using an exported Amazon S3 path would also require a separate export system outside the requested managed Databricks ingestion experience. Therefore, option A is the low-code capability that directly satisfies the Salesforce ingestion requirement.

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 Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. References: Best practices: pools | Databricks on AWS, Best practices: pools - Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google Cloud