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

Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB

Last Update 11 hours ago Total Questions : 194

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Question # 11

Your company develops an application named App1 that uses the Azure Cosmos DB SDK and the Eventual consistency level.

App1 queries an Azure Cosmos DB for NoSQL account named account!

You need to identify which consistency level to assign to App1 to meet the following requirements:

• Maximize the throughput of the queries generated by App1 without increasing the number of request units currently used by the queries.

• Provide the highest consistency guarantees.

Which consistency level should you identify?

Strong

Bounded Staleness

Session

Consistent Prefix

Question # 12

You have a container named container1 in an Azure Cosmos DB for NoSQL account named account1 that is set to the session default consistency level. The average size of an item in container1 is 20 KB.

You have an application named App1 that uses the Azure Cosmos DB SDK and performs a point read on the same set of items in container1 every minute.

You need to minimize the consumption of the request units (RUs) associated to the reads by App1. What should you do?

In account1, change the default consistency level to bounded staleness.

In App1, change the consistency level of read requests to consistent prefix.

In account1, provision a dedicated gateway and integrated cache

In App1, modify the connection policy settings.

Question # 13

You need to create a data store for a directory of small and medium-sized businesses (SMBs). The data store must meet the following requirements:

* Store companies and the users employed by them. Each company will have less than 1,000 users.

* Some users have data that is greater than 2 KB.

* Associate each user to only one company.

* Provide the ability to browse by company.

* Provide the ability to browse the users by company.

* Whenever a company or user profile is selected, show a details page for the company and all the related users.

* Be optimized for reading data.

Which design should you implement to optimize the data store for reading data?

In a directory container, create a document for each company and a document for each user. Use company ID as the partition key.

In a company container, create a document for each company. Embed the users into company documents. Use the company ID as the partition key.

Create a user container that uses the user ID as the partition key and a company container that container that uses the company ID as the partition key. Add the company ID to each user documents.

In a user container, create a document for each user. Embed the company into each user document. Use the user ID as the partition key.

Answer:

Explanation:

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.

Question # 14

You have the Azure Cosmos DB for NoSQL containers shown in the following table.

You have the items shown in the following table.

When will each item expire? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Question # 15

You have an Azure Cosmos DB for NoSQL account that has multiple write regions.

You need to receive an alert when requests that target the database exceed the available request units per second (RU/s).

Which Azure Monitor signal should you use?

Region Removed

Provisioned Throughput

Metadata Requests

Data Usage

Answer:

Explanation:

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 c reate alerts for Azure Cosmos DB using Azure Monitor based on the metrics, activity log events, or Log Analytics logs on your account 1 .

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 the Document Quota metric. 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 steps 2 :

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.

Question # 16

You have an Azure Cosmos DB for NoSQL container named Contacts that is configured as shown in the following exhibit.

Contacts contains the items shown in the following table.

To Contacts, you plan to insert the items shown in the following table.

For each of the following statements select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Question # 17

You have an Azure Cosmos DB for NoSQL account named account1.

In account 1, you plan to create a container named container1 that will contain data continuously collected from sell-driving cars. The items in container! will be accessed mostly by using queries that target data collected from a specific car during a given day. Each car is identifiable by using a property named Vehicle-Identification-Number.

You need to identify a partition strategy for container1. The strategy must meet the following requirements:

• Maximize read and write performance.

• Minimize the costs of data reads and writes.

What should you identify?

Use a partition key based on the vehicle-identification-Number property.

Construct a synthetic key by concatenating the current date and a random suffix.

Use a partition key based on the current date.

Construct a synthetic key by concatenating the current date and a hash of the Vehicle-identification-Number property value.

Question # 18

You plan to store order data in Azure Cosmos DB for NoSQL account. The data contains information about orders and their associated items.

You need to develop a model that supports order read operations. The solution must minimize the number or requests.

Create a single database that contains one container. Store orders and order items in separate documents in the container.

Create a single database that contains one container. Create a separate document for each order and embed the order items into the order documents.

Create a database for orders and a database for order items.

Create a single database that contains a container for order and a container for order items.

Answer:

Explanation:

Azure Co smos DB is a multi-model database 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 differ ent types of data in a flexible and efficient way 3 .

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 ID 1 .

It avoids storing redundant data or creating additional containers for order items 1 .

It allows you to view the order history of a customer with simple queries 1 .

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

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 MB 2 .

It may not be optimal for scenarios where order items need to be queried independently from orders or ag gregated by other criteria 2 .

It may not support transactions across multiple orders or customers, as transactions are scoped to a single logical partition 2 .

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 data 2 , or you could use a graph data model that expresses entities and relationships as vertices and edges.

Question # 19

The settings for a container in an Azure Cosmos DB Core (SQL) API account are configured as shown in the following exhibit.

Which statement describes the configuration of the container?

All items will be deleted after one year.

Items stored in the collection will be retained always, regardless of the items time to live value.

Items stored in the collection will expire only if the item has a time to live value.

All items will be deleted after one hour.

Question # 20

You have an Azure Cosmos DB account that hosts a single container named Container1. Container1 contains items that include two properties named Tenantld and Userld. Container1 has logical partitions based on the Tenantld property.

You need to configure subpartitioning for Container1 based on the Userld property. The solution must maximize data availability.

What should you do first?

Enable the analytical store for Container1.

Create a composite index.

Create a new container.

Create a stored procedure.