Scenario: Develop an app named App2 that will run from the retail stores and query the data in account2. App2 must be limited to a single DNS endpoint when accessing account2.
By using Azure Private Link, you can connect to an Azure Cosmos account via a private endpoint. The private endpoint is a set of private IP addresses in a subnet within your virtual network.
When you're using Private Link with an Azure Cosmos account through a direct mode connection, you can use only the TCP protocol. The HTTP protocol is not currently supported.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-configure-private-endpoints

Explanation
Azure Cosmos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family3. The core content-model of Cosmos DB's database engine is based on atom-record-sequence (ARS), which allows it to store and query different types of data in a flexible and efficient way3.
To develop a model that supports order read operations and minimizes the number of requests, you should consider the following factors:
* The size and shape of your data
* The frequency and complexity of your queries
* The latency and throughput requirements of your application
* The trade-offs between storage efficiency and query performance
Based on these factors, one possible model that you could implement is B. Create a single database that contains one container. Create a separate document for each order and embed the order items into the order documents.
This model has the following advantages:
* It stores orders and order items as self-contained documents that can be easily retrieved by order ID1.
* It avoids storing redundant data or creating additional containers for order items1.
* It allows you to view the order history of a customer with simple queries1.
* It leverages the benefits of embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.
This model also has some limitations, such as:
* It may not be suitable for some order items that have data that is greater than 2 KB, as it could exceed the maximum document size limit of 2 MB2.
* It may not be optimal for scenarios where order items need to be queried independently from orders or aggregated by other criteria
* It may not support transactions across multiple orders or customers, as transactions are scoped to a single logical partition2.
Depending on your specific use case and requirements, you may need to adjust this model or choose a different one. For example, you could use a hybrid data model that combines embedding and referencing data2
, or you could use a graph data model that expresses entities and relationships as vertices and edges.

Explanation

Box 1: Yes
"path": "/*" is in includePaths.
Include the root path to selectively exclude paths that don't need to be indexed. This is the recommended approach as it lets Azure Cosmos DB proactively index any new property that may be added to your model.
Box 2: No
"path": "/firstName/?" is in excludePaths.
Box 3: Yes
"path": "/address/city/?" is in includePaths
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/index-policy

Explanation
Azure Cosmos DB offers two different capacity modes: provisioned throughput and serverless1. Provisioned throughput mode allows you to configure a certain amount of throughput (expressed in Request Units per second or RU/s) that is provisioned on your databases and containers. You get billed for the amount of throughput you've provisioned, regardless of how many RUs were consumed1. Serverless mode allows you to run your database operations without having to configure any previously provisioned capacity. You get billed for the number of RUs that were consumed by your database operations and the storage consumed by your data1.
To create a database that minimizes costs, you should consider the following factors:
* The read and write volumes of your containers
* The predictability and variability of your traffic
* The latency and throughput requirements of your application
* The geo-distribution and availability needs of your data
Based on these factors, one possible option that you could choose is B. Create a provisioned throughput account. Set the throughput for coll1 to Autoscale. Set the throughput for coll2 and coll3 to Manual.
This option has the following advantages:
* It allows you to handle unpredictable read and write volumes for coll1 by using Autoscale, which automatically adjusts the provisioned throughput based on the current load1.
* It allows you to handle predictable read and write volumes for coll2 and coll3 by using Manual, which lets you specify a fixed amount of provisioned throughput that meets your performance needs1.
* It allows you to optimize your costs by paying only for the throughput you need for each container1.
* It allows you to enable geo-distribution for your account if you need to replicate your data across multiple regions1.
This option also has some limitations, such as:
* It may not be suitable for scenarios where all containers have intermittent or bursty traffic that is hard to forecast or has a low average-to-peak ratio1.
* It may not be optimal for scenarios where all containers have low or sporadic traffic that does not justify provisioned capacity1.
* It may not support availability zones or multi-master replication for your account1.
Depending on your specific use case and requirements, you may need to choose a different option. For example, you could use a serverless account if all containers have low or sporadic traffic that does not require predictable performance or geo-distribution1. Alternatively, you could use a provisioned throughput account with Manual for all containers if all containers have stable and consistent traffic that requires predictable performance or geo-distribution1.