Explanation

Box 1: Serverless capacity mode
Azure Cosmos DB serverless best fits scenarios where you expect intermittent and unpredictable traffic with long idle times. Because provisioning capacity in such situations isn't required and may be cost-prohibitive, Azure Cosmos DB serverless should be considered in the following use-cases:
Getting started with Azure Cosmos DB
Running applications with bursty, intermittent traffic that is hard to forecast, or low (<10%) average-to-peak traffic ratio Developing, testing, prototyping and running in production new applications where the traffic pattern is unknown Integrating with serverless compute services like Azure Functions Box 2: Provisioned throughput capacity mode and autoscale throughput The use cases of autoscale include:
Variable or unpredictable workloads: When your workloads have variable or unpredictable spikes in usage, autoscale helps by automatically scaling up and down based on usage. Examples include retail websites that have different traffic patterns depending on seasonality; IOT workloads that have spikes at various times during the day; line of business applications that see peak usage a few times a month or year, and more. With autoscale, you no longer need to manually provision for peak or average capacity.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/serverless
https://docs.microsoft.com/en-us/azure/cosmos-db/provision-throughput-autoscale#use-cases-of-autoscale

Explanation

Box 1: ConsistencyLevel
The ItemRequestOptions Class ConsistencyLevel property gets or sets the consistency level required for the request in the Azure Cosmos DB service.
Azure Cosmos DB offers 5 different consistency levels. Strong, Bounded Staleness, Session, Consistent Prefix and Eventual - in order of strongest to weakest consistency.
Box 2: _etag
The ItemRequestOptions class helped us implement optimistic concurrency by specifying that we wanted the SDK to use the If-Match header to allow the server to decide whether a resource should be updated. The If-Match value is the ETag value to be checked against. If the ETag value matches the server ETag value, the resource is updated.
Reference:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.cosmos.itemrequestoptions
https://cosmosdb.github.io/labs/dotnet/labs/10-concurrency-control.html

Explanation
Box 1 = Randomized
Box 2 = Deterministic
Always Encrypted for Azure Cosmos DB supports two types of encryption: deterministic and randomized1.
Deterministic encryption always produces the same encrypted value for any given plain text value.
Randomized encryption produces a different encrypted value for the same plain text value.
For dateOfBirth, randomized encryption is the strongest type of encryption because it provides better protection against statistical analysis and brute-force attacks. Deterministic encryption would not be suitable for dateOfBirth because it could reveal patterns or allow equality comparisons1.
For healthStatus, deterministic encryption is the strongest type of encryption because it allows queries to perform equality comparisons and filters on the encrypted property. Randomized encryption would not be suitable for healthStatus because it would prevent any queries on the encrypted property1.

Explanation
Based on the characteristics of the application and the provided document structure, the most suitable partition key value for coll1 in the given scenario would be the customerId, Option B.
The application frequently creates new orders by different customers and customers often view their past order history. Using customerId as the partition key would ensure that all orders associated with a particular customer are stored in the same partition. This enables efficient querying of past order history for a specific customer and reduces cross-partition queries, resulting in lower costs and improved performance.
a partition key is a JSON property (or path) within your documents that is used by Azure Cosmos DB to distribute data among multiple partitions3. A partition key should have a high cardinality, which means it should have many distinct values, such as hundreds or thousands1. A partition key should also align with the most common query patterns of your application, so that you can efficiently retrieve data by using the partition key value1.
Based on these criteria, one possible partition key that you could use for coll1 is B. customerId.
This partition key has the following advantages:
* It has a high cardinality, as each customer will have a unique ID
* It aligns with the query patterns of the application, as customers will often view their past order history3.
* It minimizes costs, as it reduces the number of cross-partition queries and optimizes the storage and throughput utilization1.
This partition key also has some limitations, such as:
* It may not be optimal for scenarios where orders need to be queried independently from customers or aggregated by date or other criteria
* It may result in hot partitions or throttling if some customers create orders more frequently than others or have more data than others
* It may not support transactions across multiple customers, as transactions are scoped to a single logical partition2.
Depending on your specific use case and requirements, you may need to adjust this partition key or choose a different one. For example, you could use a synthetic partition key that concatenates multiple properties of an item2, or you could use a partition key with a random or pre-calculated suffix to distribute the workload more evenly2.

Explanation
The solution is incorrect because there is no "isTom" parameter in the Azure Cosmos DB SDK. The correct parameter is "isTombstone".