Explanation

Box 1: Yes
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With a hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Box 2: No
Table.SelectRows returns a table of rows from the table, that matches the selection condition.
Box 3: Yes
Azure Data Lake Storage has higher throughput and IOPS.
Note: Azure Blob Storage is a general purpose, scalable object store that is designed for a wide variety of storage scenarios. Azure Data Lake Storage is a hyper-scale repository that is optimized for big data analytics workloads.
Azure Data Lake Storage use Cases: Batch, interactive, streaming analytics and machine learning data such as log files, IoT data, click streams, large datasets Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/powerquery-m/table-selectrows
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-comparison-with-blob-storage