DP-420 Exam Dumps - Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB

According to the Azure Cosmos DB documentation12, the change feed is a persistent record of changes to a container in the order they occur. The change feed outputs the sorted list of documents that were changed in the order in which they were modified.

The Azure function that has a trigger on the change feed receives all the properties of the updated items2. The change feed does not include the original items or only the changed properties. The change feed also includes some system-defined properties such as _ts (the last modified timestamp) and _lsn (the logical sequence number)3.

Therefore, the correct answer is:

A. all the properties of the updated items

With an upsert operation we can either insert or update an existing record at the same time.

FeedPollDelay: The time (in milliseconds) for the delay between polling a partition for new changes on the feed, after all current changes are drained. Default is 5,000 milliseconds, or 5 seconds.

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 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.

Box 1 = On (no default)For con-iot1, you need to configure the default TTL setting to On (no default), which means that items in this container do not expire by default, but you can override the TTL value on a per-item basis. This meets the requirement of retaining all telemetry data unless overridden1.

Box 2 = On (3600 seconds)For con-iot2, you need to configure the default TTL setting to On (3600 seconds), which means that items in this container will expire 3600 seconds (one hour) after their last modified time. This meets the requirement of deleting all telemetry data older than one hour1.

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.

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.

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.

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.