Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

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.

 The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.

The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.