DP-420 Questions and Answers

you can migrate data from various data sources to Azure Cosmos DB using different tools and methods.  The choice of the migration tool depends on factors such as th e data source, the Azure Cosmos DB API, the size of data, and the expected migration duration 1 . Some of the common migration tools are:

Azure Cosmos DB Data Migration tool: This is an open source tool that can import data to Azure Cosmos DB from sources such as JSON files, MongoDB, SQL Server, CSV files, and Azure Cosmos DB collections.  This tool supports the SQL API and the Table API of Azure Cosmos DB 2 .

Azure Data Factory: This is a cloud-based data integration service that can copy data from various sources to Azure Cosmos DB using connectors.  This tool supports the SQL API, MongoDB API, Cassandra API, Gremlin API, and Table API of Azure Cosmos DB 3 .

Azure Cosmos DB live data migrator: This is a command-line tool that can migrate data from one Azure Cosmos DB container to another container within the same or different account.  This tool supports live migration with minimal downtime and works with any Azure Cosmos DB API 4 .

For your scenario, if you want to migrate data from a third-party application that is exposed through an OData endpoint to a container in Azure Cosmos DB for NoSQL, you should use  Azure Data Factory .  Azure Data Factory has an OData connector that can read data from an OData source and write it to an Azure Cosmos DB sink using the SQL API 5 . You can create a copy activity in Azure Data Factory that specifies the OData source and the Azure Cosmos DB sink, and run it on demand or on a schedule.

Step 1: Configure the MaxItemCount in QueryRequestOptions

You can specify the maximum number of items returned by a query by setting the MaxItemCount. The MaxItemCount is specified per request and tells the query engine to return that number of items or fewer.

Box 2: Run the query and provide a continuation token

In the .NET SDK and Java SDK you can optionally use continuation tokens as a bookmark for your query ' s progress. Azure Cosmos DB query executions are stateless at the server side and can be resumed at any time using the continuation token.

If the query returns a continuation token, then there are additional query results.

Step 3: Append the results to a variable

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 account 2 , which is strong in this case.  Strong consiste ncy ensures that every write operation is synchronously committed to every region associated with your Azure Cosmos DB account 1 .  The request level consistency level of session only applies to the read operations of App1 1 .

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.

Box 1: CompanyID

After you create a container with a unique key policy, the creation of a new or an update of an existing item resulting in a duplicate within a logical partition is prevented, as specified by the unique key constraint. The partition key combined with the unique key guarantees the uniqueness of an item within the scope of the container.

For example, consider an Azure Cosmos container with Email address as the unique key constraint and CompanyID as the partition key. When you configure the user ' s email address with a unique key, each item has a unique email address within a given CompanyID. Two items can ' t be created with duplicate email addresses and with the same partition key value.

Box 2: emailAddress

Instead configure an Azure Monitor alert to trigger the function.

You can set up alerts from the Azure Cosmos DB pane or the Azure Monitor service in the Azure portal.

Azure Cosmos DB is a multi-model databas e that supports various data models, such as documents, key-value, graph, and column -family 3 .  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 efficien t way 3 .

To optimize the data store for reading data, 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 design that you could implement is  B. In a company container, create a document for each company. Embed the users into company documents. Use the company ID as the partition key .

This design has the following advantages:

It stores companies and users as self-contained documents that can be easily retrieved by company ID 1 .

It avoids storing redundant data or creat ing additional containers for users 1 .

It allows you to browse b y company and browse the users by company with simple queries 1 .

It shows a details page for the company and all the related users by fetching a single document 1 .

It leverages the benefits of embedding data, such as reducing the number of requests , improving query performance, and simplifying data consistency 2 .

This design also has some limitations, such as:

It may not be suitable for some users who have data that is greater than 2 KB, as it could exc eed the maximum document size limit of 2 MB 2 .

It may not be optimal for scenarios where users need to be associated with more than one company or queried independently from companies 2 .

It may not be scalable for companies that have more than 1,000 users, as it could result in hot partitions or throttling 2 .

Depending on your specific use case and requirements, you may need to adjust this design or choose a different one.  For example, you could use a hybrid data model that combines embedding and referencing data 2 , or you could use a graph data model that expresses entities and relationships as vertices and edges.

Bounded staleness is frequently chosen by globally distributed applications that expect low write latencies but require total global order guarantee. Bounded staleness is great for applications featuring group collaboration and sharing, stock ticker, publish-subscribe/queueing etc.

Scenario: Application updates in con-product frequently cause HTTP status code 429 " Too many requests " . You discover that the 429 status code relates to excessive request unit (RU) consumption during the updates.

The partition key is what will determine how data is routed in the various partitions by Cosmos DB and needs to make sense in the context of your specific scenario. The IoT Device ID is generally the " natural " partition key for IoT applications.

Scenario: The iotdb database will contain two containers named con-iot1 and con-iot2.

Ensure that Azure Cosmos DB costs for IoT-related processing are predictable.

leaseCollectionPrefix: when set, the value is added as a prefix to the leases created in the Lease collection for this Function. Using a prefix allows two separate Azure Functions to share the same Lease collection by using different prefixes.

Scenario: Use Azure Functions to send notifications about product updates to different recipients.

Trigger the execution of two Azure functions following every update to any document in the con-product container.

App1 queries the con-product and con-productVendor containers.

Note: Request unit is a performance currency abstracting the system resources such as CPU, IOPS, and memory that are required to perform the database operations supported by Azure Cosmos DB.

Scenario:

Develop an app named App1 that will run from all locations and query the data in account1.

Once multi-region writes are configured, maximize the performance of App1 queries against the data in account1.

Whenever there are multiple solutions for a requirement, select the solution that provides the best performance, as long as there are no additional costs associated.

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.