Pass the Microsoft Certified: Azure Cosmos DB Developer Specialty DP-420 Questions and answers with ValidTests

Box 1: No

Azure Cosmos DB's change feed is a great choice as a central data store in event sourcing architectures where all data ingestion is modeled as writes (no updates or deletes).

Note: The change feed does not capture deletes. If you delete an item from your container, it is also removed from the change feed. The most common method of handling this is adding a soft marker on the items that are being deleted. You can add a property called "deleted" and set it to "true" at the time of deletion. This document update will show up in the change feed. You can set a TTL on this item so that it can be automatically deleted later.

Box 2: No

The _etag format is internal and you should not take dependency on it, because it can change anytime.

Box 3: Yes

Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos container for any changes.

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 criteria2.

It may not supporttransactions 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.

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.

Use a partition key with a random suffix. Distribute the workload more evenly is to append a random number at the end of the partition key value. When you distribute items in this way, you can perform parallel write operations across partitions.

The Azure Cosmos DB change feed is a mechanism to get a continuous and incremental feed of records from an Azure Cosmos container as those records are being created or modified. Change feed support works by listening to container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.

The following diagram represents the data flow and components involved in the solution:

You can use the Set-AzCosmosDBAccountRegion cmdlet to update the regions that an Azure Cosmos DB account uses. You can use this cmdlet to add a region or change the region failover order. The cmdlet requires a resource group name, an Azure Cosmos DB account name, and a list of regions in desired failover order1.

For your scenario, based on the PowerShell command, you are using the Set-AzCosmosDBAccountRegion cmdlet to update the regions for an Azure Cosmos DB account named account1 that is configured for automatic failover. The command specifies two regions: West US and East US. The effect of running the command is that the account will be configured for multi-region writes.

Multi-region writes is a feature of Azure Cosmos DB that allows you to write data to any region in your account and have it automatically replicated to all other regions. This feature provides high availability and low latency for write operations across multiple regions. To enable multi-region writes, you need to specify at least tworegions in your account and set them as write regions2. In your command, you are setting both West US and East US as write regions by using the -IsZoneRedundant parameter with a value of $true for both regions.

Box 1 =Write and replicate data to every region synchronously

This is because the write concern is mapped to the default consistency level configured on your Azure Cosmos DB account2, which is strong in this case. Strong consistency ensures that every write operation is synchronously committed to every region associated with your Azure Cosmos DB account1. The request level consistency level of session only applies to the read operations of App11.

Box 2:That has the lowest estimated latency to the client

This is because the read operations of App1 will use the session consistency level that is specified in the request options. Session consistency is a client-centric consistency model that guarantees monotonic reads, monotonic writes, and read-your-own-writes within a session. A session is scoped to a client connection or a stored procedure execution. Session consistency allows clients to read from any region that has the lowest latency to the client.