Databricks Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam

The best practice is to use " Complete " as output mode instead of " append " when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

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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Reading a Unity Catalog table requires privileges at all relevant levels of its three-part namespace. The group needs USE CATALOG on corp_marketing, USE SCHEMA on campaigns, and SELECT on email_stats. These privileges allow users to resolve the catalog and schema and read the table without granting permission to create or modify objects. Option A omits USE CATALOG, so table access is incomplete. Option B unnecessarily grants CREATE SCHEMA, violating the requirement that the group must not create objects. Option D omits USE CATALOG and grants MODIFY, which would permit data changes. Unity Catalog follows a hierarchical permissions model, but possession of SELECT alone is insufficient without the required usage privileges on the parent containers. Therefore, the least-privilege sequence in option C is correct.

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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

Option D is directly associated with the failure because toPandas() collects the complete Spark DataFrame into a single pandas DataFrame in the driver process. With 50 million wide rows, the collected result can exceed the memory assigned to the Spark driver and produce a driver-side OutOfMemoryError. Databricks and PySpark documentation warn that toPandas() should be used only when the resulting dataset is expected to be small because all records are loaded into driver memory. spark.executor.memory controls the executor processes that perform distributed transformations, not the final local pandas object. spark.sql.shuffle.partitions controls the number of shuffle partitions, while spark.memory.fraction controls Spark execution and storage memory allocation. Therefore, the relevant configuration among the available choices is spark.driver.memory, making option D correct.

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 To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other’s outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:

Task A: Ingest data from a source using Auto Loader

Task B: Transform the data using Spark SQL

Task C: Write the data to a Delta Lake table

Task D: Analyze the data using Spark ML

Task E: Visualize the data using Databricks SQL

In this case, the data engineer can set the dependencies of each task as follows:

Task A: No dependencies

Task B: Depends on Task A

Task C: Depends on Task B

Task D: Depends on Task C

Task E: Depends on Task D

This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.

The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:

Whether the task needs to be replaced by another task

Whether the task needs to fail before another task begins

Whether the task has the same dependency libraries as another task

Whether the task needs to use as little compute resources as possible References: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

This question’s options do not include the most precise Unity Catalog answer, which would normally be the MANAGE privilege on the schema, because Databricks documents that MANAGE allows a principal to grant and revoke privileges on an object without automatically granting all data-access privileges on that object. However, since MANAGE is not one of the answer choices, the best available answer is D, Schema Owner . Databricks states that schema owners can grant and revoke permissions on the schema, and schema-level administration is a documented responsibility of the schema owner. Option A is incorrect because USE CATALOG and USE SCHEMA only allow object access/navigation and do not confer privilege-management rights. Option B is too broad because a catalog owner governs the entire catalog scope, not just one schema. Option C is too narrow because a table owner controls only one table, not various objects across a schema. So, among the choices provided, Schema Owner is the closest Databricks-aligned answer, though the cleanest real-world implementation would be MANAGE on the schema .

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Option D uses the business-key columns as the subset for duplicate comparison. Databricks documents that DataFrame.dropDuplicates can optionally accept a list of columns and then removes rows that are duplicates with respect to only those columns. Therefore, differing ingestion_timestamp values do not prevent records with the same customer_id and email from being treated as duplicates. Calling distinct() compares complete rows, so timestamp differences would preserve multiple records. Selecting only customer_id and email before distinct would remove every other column from the Silver result. The groupBy and max expression calculates a latest timestamp per key, but it changes the output shape and does not preserve a complete source record unless additional logic is added. For the stated requirement, option D is the direct and correct operation.

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