Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse.Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1.Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2.Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3.Reference:1:What is Delta Lake? | Databricks on AWS2:Upsert into a table using merge | Databricks on AWS3: [Query an older snapshot of a table (time travel) | Databricks on AWS]

Learn more

1learn.microsoft.com2medium.com3slideshare.net4docs.databricks.com5github.com6key2consulting.com

In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon.Reference:Data Engineer Associate Exam Guide,CREATE TABLE USING (Databricks SQL)

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]

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables.Reference:

What is the medallion lakehouse architecture?

What is a Medallion Architecture?

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.Reference:Difference between LIVE TABLE and STREAMING LIVE TABLE,CREATE STREAMING TABLE,Load data using streaming tables in Databricks SQL.