Final Answer : D

A: Apache Spark,it's in Azure Synapse Analytics is an analytics engine that facilitates large-scale data processing. It can read data from Cosmos DB in near-real-time, process it, and then output the results to a data warehouse in the same Azure Synapse Analytics workspace. This minimizes data movement as the data processing and storage are happening within the same service (Azure Synapse Analytics).

Apache Spark is a distributed processing framework that can handle near-real-time processing and is well-integrated with Azure Synapse Analytics. It can directly access data stored in Azure Cosmos DB analytical store without needing to move the data around. This minimizes data movement and provides efficient processing capabilities. So, the correct answer is: A. Apache Spark

A is the correct answer https://learn.microsoft.com/en-us/azure/cosmos-db/synapse-link-use-cases

I go with A: Two reasons: Synapse had Apache Spark, Dat explore is mainly for logs, refer to the bottom flowchart of the below link https://learn.microsoft.com/en-us/azure/data-explorer/data-explorer-overview

Final Answer : D

Dedicated SQL pools are specifically designed for low-latency analytical workloads, making them ideal for processing data in near-real-time.

Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management. https://learn.microsoft.com/en-us/azure/data-explorer/data-explorer-overview https://learn.microsoft.com/en-us/azure/synapse-analytics/data-explorer/data-explorer-overview

I recommend using a dedicated SQL pool Near-real-time processing: Dedicated SQL pools are specifically designed for low-latency analytical workloads, making them ideal for processing data in near-real-time. Data minimization: Dedicated SQL pools are integrated with Workspace1, allowing for seamless data movement between the Cosmos DB analytical store and the data warehouse within the same workspace. This minimizes data movement and avoids the need for external data transfer processes. Runtime engine: Dedicated SQL pools provide a T-SQL compatible query engine that can be used to interact with data stored in the data warehouse. This allows you to leverage familiar SQL syntax for data transformation and analysis.

because of "near-real-time"

B. Serverless SQL pool Reasoning: Serverless SQL pool in Azure Synapse Analytics is designed to handle on-demand queries against large datasets, which is suitable for the NRT processing requirement stated. Minimal Data Movement: Using serverless SQL pool allows querying data in place without the need to move data into the pool, which aligns with the need to minimize data movement. It can directly query the Cosmos DB analytical store. Integration with Cosmos DB Analytical Store: Serverless SQL pool has built-in integration with Azure Cosmos DB's analytical store, allowing efficient and performant processing of the data. Apache Spark could also process the data, but it would involve more data movement compared to serverless SQL. Dedicated SQL pool requires pre-provisioned resources and wouldn't be as cost-effective for NRT scenarios. Data Explorer is not a compute pool within Azure Synapse Analytics.