Selected Databricks-Certified-Data-Engineer-Associate Databricks Certification Questions Answers

 A streaming live table or view processes data that has been added only since the last pipeline update. Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

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 query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:

• Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
• Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
• Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
• Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.

References:Databricks documentation on Structured Streaming: Structured Streaming in Databricks

 To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:

• GRANT
• Privileges