Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/analytical-data-stores

Explanation:

Box 1: Yes
Azure Databri cks can consume data from SQL Databases using JDBC and from SQL Databases using the Apache Spark connector.
The Apache Spark connector for Azure SQL Database and SQL Server enables these databases to act as input data sources and output data sinks for Apac he Spark jobs.
Box 2: Yes
You can stream data into Azure Databricks using Event Hubs.
Box 3: Yes
You can run Spark jobs with data stored in Azure Cosmos DB using the Cosmos DB Spark connector.
Cosmos can be used for batch and stream processing, and as a serving layer for low latency access.
You can use the connector with Azure Databricks or Azure HDInsight, which provide managed Spark clusters on Azure.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/data/data-s ources/sql-databases-azure
https://docs.microsoft.com/en-us/azure/datab ricks/scenarios/databricks-stream-from-eventhubs

Explanation:

Box 1: No
The API determines the type of account to create. Azure Cosmos DB provides five APIs: Core (SQL) and MongoDB for document data, Gremlin for graph data, Azure Table, and Cassandra. Currently, you must create a separate account for each API.
Box 2: Yes
Azure Cosmos DB uses partitioning to scale individual containers in a database to meet the performance needs of your application. In partitioning, the items in a container are divided into distinct subsets called logical partitions. Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Box 3: No
Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview

Reference:
https://www.sqlshack.com/start-your-journey-with-azure-cosmos-db/