This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" 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 References:
[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

Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.

Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, thedatecolumn is a good candidate for partitioning for several reasons:
* Time-Based Queries: If queries frequently filter or group by date, partitioning by thedatecolumn can significantly improve performance by limiting the amount of data scanned.
* Granularity: Thedatecolumn likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
* Data Skew: Other columns likepost_idoruser_idmight lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning bypost_timecould also be considered, but typicallydateis preferred due to its more manageable granularity.
References:
* Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning

Explanation
The answer is Single job can be used to set up both notebook and DLT pipeline, use two different tasks with linear dependency, Here is the JOB UI
1.Create a notebook task
2.Create DLT task
a.add notebook task as dependency
3.Final view
Create the notebook task
Graphical user interface, text, application, email Description automatically generated

DLT task
Graphical user interface, text, application, email Description automatically generated

Final view
Graphical user interface, text, application, PowerPoint Description automatically generated

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