Data Manipulation Language (DML) affect the information stored in the database. Use these statements to insert, update, and change the rows in the database.
- BULK INSERT
- DELETE
- INSERT
- SELECT
- UPDATE
- MERGE
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/statements

Explanation:
Azure Synapse Analytics is an platform as a service (PAAS) that combines data integration, warehousing, and analytics into one solution. Azure Synapse Analytics offers cloud-based, relational data warehousing services, massively parallel processing (MPP) scale-out technology, and enough computational power to eficiently manage petabytes and petabytes of data.
Incorrect:
Azure Database for MariaDB is a relational database service based on the open-source MariaDB Server engine. It's a fully managed database as a service offering that can handle mission-critical workloads with predictable performance and dynamic scalability.
Azure SQL Database is a fully managed platform as a service (PaaS) database engine that handles most of the database management functions such as upgrading, patching, backups, and monitoring without user involvement. Azure SQL Database is always running on the latest stable version of the SQL Server database engine and patched OS with 99.99% availability. PaaS capabilities built into Azure SQL Database enable you to focus on the domain-specific database administration and optimization activities that are critical for your business.
Reference:
https://cswsolutions.com/blog/posts/2021/august/what-is-azure-synapse-analytics/
https://www.integrate.io/blog/what-is-azure-synapse-analytics

Explanation:
Distributions map to Compute nodes for processing. As you pay for more compute resources, distributions are remapped to available Compute nodes.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/massively-parallel- processing-mpp-architecture [see the architecture diagram]

Data as it appears in the real world is naturally connected. Traditional data modeling focuses on defining entities separately and computing their relationships at runtime. While this model has its advantages, highly connected data can be challenging to manage under its constraints.
A graph database approach relies on persisting relationships in the storage layer instead, which leads to highly efficient graph retrieval operations. Azure Cosmos DB's Gremlin API supports the property graph model.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction#introduction-to-graph- databases

https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-overview