Execute (X) permission is required to traverse the child items of a folder.
There are two kinds of access control lists (ACLs), Access ACLs and Default ACLs.
Access ACLs: These control access to an object. Files and folders both have Access ACLs.
Default ACLs: A "template" of ACLs associated with a folder that determine the Access ACLs for any child items that are created under that folder. Files do not have Default ACLs.
Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-access-control

Synapse workspaces encryption uses existing keys or new keys generated in Azure Key Vault. A single key is used to encrypt all the data in a workspace. Synapse workspaces support RSA 2048 and 3072 byte-sized keys, and RSA-HSM keys.
The Key Vault itself needs to have purge protection enabled.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/security/workspaces-encryption

The alter row transformation allows you to specify insert, update, delete, and upsert policies on rows based on expressions. You can use the alter row transformation to perform upserts on a sink table by matching on a key column and setting the appropriate row policy

C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption

Hash-distributed tables improve query performance on large fact tables.
Columnstore indexes can achieve up to 100x better performance on analytics and data warehousing workloads and up to 10x better data compression than traditional rowstore indexes.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-query-performance