Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certification Questions and Answers

When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.

Option B keeps environment differences entirely in the bundle configuration. Declarative Automation Bundles support custom variables that can be referenced by resource definitions, making the same job and notebook definitions reusable. Databricks targets represent deployment environments such as development, test, and production, and target-level settings take precedence over top-level settings, including variable overrides. This allows the team to deploy the same codebase while supplying different workspace paths, compute identifiers, catalog names, or other values for each environment. Editing deployed jobs in the workspace UI creates configuration drift and breaks the bundle as the authoritative source. Separate Git branches duplicate configuration and promotion logic, while notebook parameters move environment logic into application code. Therefore, defining variables and overriding them through targets is the correct configuration-only approach.

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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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https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html

The command DESCRIBE DATABASE customer360; will return the location of the database customer360, along with its comment and properties. This command is an alias for DESCRIBE SCHEMA customer360;, which can also be used to get the same information. The other commands will either drop the database, alter its properties, or use it as the current database, but will not return its location12. References:

DESCRIBE DATABASE | Databricks on AWS

DESCRIBE DATABASE - Azure Databricks - Databricks SQL

https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

Option A uses the supported ALTER TABLE ... SET MANAGED conversion for a Unity Catalog external Delta table. Databricks documents that this conversion retains the table name, permissions, dependent objects, settings, and table history while minimizing downtime for readers and writers. After conversion, Databricks manages the table’s storage lifecycle, satisfying the requirement for platform-controlled cleanup when the managed table is dropped. Dropping the original table with PURGE destroys the object instead of migrating it. SET LOCATION changes the storage path referenced by an external table but does not convert the table into a managed table. Creating a replacement table with CREATE TABLE AS SELECT introduces a new object and requires manual migration of permissions, dependencies, and workloads. Therefore, ALTER TABLE catalog.schema.sales SET MANAGED is the correct action.

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