Explanation:

Box 1: PIVOT
PIVOT rotates a table-valued expression by turning the unique values from one column in the expression into multiple columns in the output. And PIVOT runs aggregations where they're required on any remaining column values that are wanted in the final output.
Reference:
https://learnsql.com/cookbook/how-to-convert-an-integer-to-a-decimal-in-sql-server/
https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot

A: Cache used is the sum of all bytes in the local SSD cache across all nodes and cache capacity is the sum of the storage capacity of the local SSD cache across all nodes.
E: Cache hits is the sum of all columnstore segments hits in the local SSD cache and cache miss is the columnstore segments misses in the local SSD cache summed across all nodes Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-concept- resource-utilization-query-activity

According to the information I found on the web, two options that you should include in the solution to minimize how long it takes to perform queries and joins on non-partitioned tables are:
Z-Ordering: This is a technique to colocate related information in the same set of files. This co-locality is automatically used by Delta Lake in data-skipping algorithms. This behavior dramatically reduces the amount of data that Delta Lake on Azure Databricks needs to read123.
Apache Spark caching: This is a feature that allows you to cache data in memory or on disk for faster access.
Caching can improve the performance of repeated queries and joins on the same data. You can cache Delta tables using the CACHE TABLE or CACHE LAZY commands.
To minimize the time it takes to perform queries against non-partitioned tables and joins on non-partitioned columns in Delta Lake on Azure Databricks, the following options should be included in the solution:
A:Z-Ordering: Z-Ordering improves query performance by co-locating data that share the same column values in the same physical partitions. This reduces the need for shuffling data across nodes during query execution. By using Z-Ordering, you can avoid full table scans and reduce the amount of data processed.
B:Apache Spark caching: Caching data in memory can improve query performance by reducing the amount of data read from disk. This helps to speed up subsequent queries that need to access the same data. When you cache a table, the data is read from the data source and stored in memory. Subsequent queries can then read the data from memory, which is much faster than reading it from disk.
References:
Delta Lake on Databricks: https://docs.databricks.com/delta/index.html
Best Practices for Delta Lake on Databricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta- lake-on-databricks.html

Data Discovery & Classification is built into Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics. It provides basic capabilities for discovering, classifying, labeling, and reporting the sensitive data in your databases.
Your most sensitive data might include business, financial, healthcare, or personal information. Discovering and classifying this data can play a pivotal role in your organization's information-protection approach. It can serve as infrastructure for:
Helping to meet standards for data privacy and requirements for regulatory compliance.
Various security scenarios, such as monitoring (auditing) access to sensitive data.
Controlling access to and hardening the security of databases that contain highly sensitive data.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/data-discovery-and-classification-overview