Explanation
The Cosmos DB Built-in Data Contributor role provides the necessary permissions to insert items into a container in an Azure Cosmos DB for NoSQL account. This role grants the minimum required privileges for the described task, adhering to the principle of least privilege.

Explanation
the Azure Cosmos DB integrated cache is an in-memory cache that is built-in to the Azure Cosmos DB dedicated gateway. The dedicated gateway is a front-end compute that stores cached data and routes requests to the backend database. You can choose from a variety of dedicated gateway sizes based on the number of cores and memory needed for your workload1. The integrated cache can reduce the RU consumption and latency of read operations by serving them from the cache instead of the backend containers2.
For your scenario, to ensure that App1 can use the integrated cache, you should perform these two actions:
* Change the account endpoint to https://account1.sqlx.cosmos.azure.com. This is the dedicated gateway endpoint that you need to use to connect to your Azure Cosmos DB account and leverage the integrated cache. The standard gateway endpoint (https://account1.documents.azure.com) will not use the integrated cache2.
* Change the consistency level of requests to session. This is the highest consistency level that is supported by the integrated cache. If you use a higher consistency level (such as strong or bounded staleness), your requests will bypass the integrated cache and go directly to the backend containers

Explanation
Batch size: An integer that represents how many objects are being written to Cosmos DB collection in each batch. Usually, starting with the default batch size is sufficient. To further tune this value, note:
Cosmos DB limits single request's size to 2MB. The formula is "Request Size = Single Document Size * Batch Size". If you hit error saying "Request size is too large", reduce the batch size value.
The larger the batch size, the better throughput the service can achieve, while make sure you allocate enough RUs to empower your workload.
Reference: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db

Explanation

Box 1: Yes
Non-relational data increases write costs, but can decrease read costs.
Box 2: Yes
Non-relational data increases write costs, but can decrease read costs.
Box 3: No
Non-relational data increases write costs, but can decrease read costs.

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.