Databricks-Certified-Professional-Data-Engineer Exam Dumps

This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Lakehouse'' section; Databricks Documentation, under ''Database object privileges'' section.

This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''Auto Compaction for Delta Lake on Databricks'' section.

'Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously.'

https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

The code block that should be used to create the date Python variable used in the above code block is:

dbutils.widgets.text(''date'', ''null'') date = dbutils.widgets.get(''date'')

This code block uses the dbutils.widgets API to create and get a text widget named ''date'' that can accept a string value as a parameter1. The default value of the widget is ''null'', which means that if no parameter is passed, the date variable will be ''null''. However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is ''2021-11-01'', the date variable will be ''2021-11-01''. This way, the notebook can use the date variable to load data from the specified path.

The other options are not correct, because:

Option A is incorrect because spark.conf.get(''date'') is not a valid way to get a parameter passed through the Databricks Jobs API.The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.

Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API.The input() function is used to get user input from the standard input stream, not from the API request3.

Option C is incorrect because sys.argv1is not a valid way to get a parameter passed through the Databricks Jobs API.The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.

Option D is incorrect because dbutils.notebooks.getParam(''date'') is not a valid way to get a parameter passed through the Databricks Jobs API.The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.

This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Spark Configuration'' section;Databricks Documentation, under ''Available Properties - spark.sql.files.maxPartitionBytes'' section.

This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''Auto Optimize'' section. https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'