Explanation:
https://learn.microsoft.com/en-us/training/modules/intro-to-azure-data-lake-storage/1-introduction
https://learn.microsoft.com/en-us/training/modules/intro-to-azure-data-factory/2-what-is-azure- data-factory
https://learn.microsoft.com/en-us/training/modules/intro-to-azure-hdinsight/2-what-is-azure- hdinsight

Explanation:
A data analyst is responsible for identifying which business rules must be applied to the data of a company. Their role involves understanding business needs, analyzing data, and ensuring that appropriate business rules are applied to support decision-making and reporting.

A relational database stores data and provides access to data points that are related to each other. A relational database follows the basic relational model that is a simple and straightforward method of storing data in tables. Each row of the table in a relational database represents a record with a unique id i.e. key. Each column of the table holds an attribute of the data, and each record represents the value for each attribute.
For example, a table STUDENT_RECORD represents the record of students in a class and the attributes of columns are - Student_Id, Student_Name, Student_Branch, and Student_Marks. We need to define the data type of each attribute in the beginning at the time of the creation of the table. Here, if we set Student_Id as a number, all the values in the column Student_Id will contain numbers only. And if we set it as alphanumeric, all the values in the column Student_Id will contain both alphabets and numbers.

So, Option A is correct.
Reference: To know more about the characteristics of Relational Databases, click the Lin below:
https://docs.rackspace.com/support/how-to/properties-of-rdbmss-and-nosql-databases/

Expected low latency: Real-time data processing requires low latency to ensure that data is processed almost immediately after it is received.
Data is processed just after creation: Real-time processing involves handling data right after it is created to make quick decisions or take actions.

Explanation:
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.
One advantage of hierarchical namespace is atomic directory manipulation: Object stores approximate a directory hierarchy by adopting a convention of embedding slashes (/) in the object name to denote path segments.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace