Databricks-Certified-Data-Engineer-Associate Exam Questions

he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer.Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1.The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2.The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.

The other options are not correct or reliable for identifying the owner of new_table.Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4.Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.

1:Data Explorer | Databricks on AWS

2:Table details | Databricks on AWS

3:Set owner when creating a view in databricks sql - Databricks - 9978

4:Table access control | Databricks on AWS

5:External tables | Databricks on AWS

To create a table in Databricks using data from an SQLite database, the correct syntax involves specifying the format of the data source. The format in the case of using JDBC (Java Database Connectivity) with SQLite is specified by the org.apache.spark.sql.jdbc format. This format allows Spark to interface with various relational databases through JDBC. Here is how the command should be structured:

CREATE TABLE jdbc_customer360

USING org.apache.spark.sql.jdbc

OPTIONS (

url 'jdbc:sqlite:/customers.db',

dbtable 'customer360'

)

The USING org.apache.spark.sql.jdbc line specifies that the JDBC data source is being used, enabling Spark to interact with the SQLite database via JDBC.

Reference: Databricks documentation on JDBC: Connecting to SQL Databases using JDBC

dbfs:/user/hive/warehouse Thereby showing 'dbfs:/user/hive/warehouse/customer360.db

The location of the customer360 database depends on the value of thespark.sql.warehouse.dirconfiguration property, which specifies the default location for managed databases and tables. If the property is not set, the default value isdbfs:/user/hive/warehouse. Therefore, the customer360 database will be located indbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such asdbfs:/user/hive/database, then the customer360 database will be located indbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.

Option A is not correct, asdbfs:/user/hive/database/customer360is not the default location for managed databases and tables, unless thespark.sql.warehouse.dirproperty is explicitly set todbfs:/user/hive/database.

Option B is not correct, asdbfs:/user/hive/warehouseis the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with.dbto the directory path, such asdbfs:/user/hive/warehouse/customer360.db.

Option C is not correct, asdbfs:/user/hive/customer360is not a valid location for a managed database, as it does not follow the directory structure specified by thespark.sql.warehouse.dirproperty.

Databases and Tables

[Databricks Data Engineer Professional Exam Guide]

In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.

Reference: Databricks documentation on Delta Live Tables: Delta Live Tables Guide

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.

Reference: Databricks documentation on Delta Live Tables: Delta Live Tables Guide