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

 To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. References: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks

Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

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

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

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

Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.

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

Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.