Most Recent Microsoft DP-420 Exam Questions & Answers

Azure Monitor is a service that provides comprehensive monitoring for Azure resources, including Azure Cosmos DB. You can use Azure Monitor to collect, analyze, and alert on metrics and logs from your Azure Cosmos DB account.You can create alerts for Azure Cosmos DB using Azure Monitor based on the metrics, activity log events, or Log Analytics logs on your account1.

For your scenario, if you want to receive an alert when requests that target the database exceed the available request units per second (RU/s), you should use theDocument Quotametric. This metric measures the percentage of RU/s consumed by your account or container.You can create an alert rule on this metric from the Azure portal by following these steps2:

In the Azure portal, select the Azure Cosmos DB account you want to monitor.

Under the Monitoring section of the sidebar, select Alerts, and then select New alert rule.

In the Create alert rule pane, fill out the Scope section by selecting your subscription name and resource type (Azure Cosmos DB accounts).

In the Condition section, select Add condition and choose Document Quota from the list of signals.

In the Configure signal logic pane, specify the threshold value and operator for your alert condition. For example, you can choose Greater than or equal to 90 as the threshold value and operator to receive an alert when your RU/s consumption reaches 90% or more of your provisioned throughput.

In the Alert rule details section, specify a name and description for your alert rule.

In the Actions section, select Add action group and choose how you want to receive notifications for your alert. For example, you can choose Email/SMS/Push/Voice as an action type and enter your email address or phone number as a receiver.

Review your alert rule settings and select Create alert rule to save it.

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 isB. 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.

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.

https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

Setting ConflictResolutionMode to Custom and configuring a custom stored procedure with the 'conflictingItems' parameter will allow you to implement a custom conflict resolution policy. This will ensure that any conflicts are sent to the conflict feed for resolution.