Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by hasan

Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables

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.

 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.

To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:

CREATE TABLE customersInFrance

USING DELTA

TBLPROPERTIES ('PII' = 'true')

AS

SELECT id,

firstName,

lastName

FROM customerLocations

WHERE country = 'FRANCE';

The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.

References:Databricks documentation on Delta Lake: Delta Lake on Databricks