Explanation

Box 1: Yes
Big data solutions often use long-running batch jobs to filter, aggregate, and otherwise prepare the data for analysis. Usually these jobs involve reading source files from scalable storage (like HDFS, Azure Data Lake Store, and Azure Storage), processing them, and writing the output to new files in scalable storage.
Box 2: No
Box 3: No

Section: Describe how to work with non-relational data on Azure
Explanation:
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

Section: Describe how to work with non-relational data on Azure
Explanation/Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/authentication-mfa-ssms-overview