Use Azure Disk Encryption. It enables IT administrators to encrypt Windows and Linux IaaS VM disks. Disk Encryption combines the industry-standard Windows BitLocker feature and the Linux dm-crypt feature to provide volume encryption for the OS and the data disks. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/security/fundamentals/data-encryption-best-practices#protect-data-at-rest

Azure Cosmos DB supports multiple database APIs, including Cassandra, MongoDB, and Gremlin. However, MaxDB is not a supported database API format in Cosmos DB.

Why the other answers are wrong:

A. Cassandra: Azure Cosmos DB provides support for the Cassandra API, allowing you to use Cassandra query language (CQL) to interact with data.

B. MongoDB: Cosmos DB supports the MongoDB API, enabling applications to use MongoDB drivers to interact with the database.

C. Gremlin: Cosmos DB also supports the Gremlin API for graph databases, allowing you to use the Gremlin query language for graph traversal.

Example:

If Café Rivoli is considering using Azure Cosmos DB and needs to decide which database API format to use, they can choose from Cassandra, MongoDB, and Gremlin, among others. However, they cannot use MaxDB with Cosmos DB, as it is not a supported API format.

Data warehouse workloads encompass the full spectrum of activities involved in utilizing a data warehouse. This includes not only the initial data loading but also the ongoing processes of querying, analyzing, reporting, managing, and exporting data.

Here's why the other options are incorrect:

A. The data contained in a SQL Data Warehouse: This refers to the content of the data warehouse, not the workloads acting upon it.

B. The amount of storage required to keep the data in Azure: This is a factor in infrastructure, not the workload itself.

C. The processing required to run queries against a SQL Data Warehouse: While query processing is a significant part of data warehouse workloads, it doesn't encompass the entire scope of activities involved.

Example:

Consider a data warehouse for a retail company. The workload would include:

[+] Loading: Importing sales data, inventory data, and customer data from various sources into the data warehouse.

[+] Analyzing: Running queries to identify trends in sales, customer behavior, or inventory levels.

[+] Reporting: Generating reports on sales performance, customer demographics, or inventory forecasts.

[+] Managing: Maintaining and optimizing the data warehouse for performance and efficiency.

[+] Exporting: Extracting data for further analysis or integration with other systems.

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. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/cosmos-db/account-databases-containers-items

Primary keys provide access to all the administrative resources for the database account. Each account consists of two primary keys: a primary key and secondary key. The purpose of dual keys is to let you regenerate, or roll keys, providing continuous access to your account and data. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/cosmos-db/database-security#primary-keys

Data in transit: Data while it is being transferred between components, locations or programs, such as over the network, across a service bus (from on-premises to cloud and vice-versa), or during an input/output process. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/securing-data-solutions

Both platforms can perform similar tasks, such as scaling, parallel processing and ingesting data from various sources. Synapse Analytics also supports SQL syntax, which will be familiar for developers of SQL Server. Refer to Microsoft Doc:

https://azure.microsoft.com/en-ca/services/synapse-analytics/#key-service-capabilities

Data movement, Data transformation, and Control are the three types of activities that Azure Data Factory supports. Copy Data is the Data Movement activity, while MapReduce and Stored Procedure are both transformation activities. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipelines-activities

Structured data is organized in a fixed schema or format, making it easily searchable and analyzable. The contents of a SQL database table are structured data because they are stored in a predefined schema with rows and columns, where each column has a specific data type.

Why the other answers are wrong:

A. Excel spreadsheet: While Excel spreadsheets can contain structured data, they can also include unstructured data, such as free-form text or mixed data types. The format is not as rigid as a SQL database table.

B. Blob files: Blob files (Binary Large Objects) can store any type of data, including unstructured data such as images, videos, or binary files.

C. JSON data: JSON (JavaScript Object Notation) data is semi-structured. It has a flexible schema and can contain nested objects and arrays, making it less rigid than structured data but more organized than unstructured data.

Example:

The contents of a SQL database table for Café Rivoli’s customer information might include columns like CustomerID, Name, Email, and PhoneNumber. Each row in the table follows this predefined structure, making it easy to query and analyze the data using SQL.

Document data is data that is stored in a readable format, such as JSON. Using Cosmos DB Core SQL API, you can query this data. The database does not enforce a schema on the data. It scales for a global scale. Refer to Microsoft Doc:

https://docs.microsoft.com/en-gb/azure/architecture/data-guide/big-data/non-relational-data