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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q113-Q118):

NEW QUESTION # 113
You need to implement a trigger in Azure Cosmos DB Core (SQL) API that will run before an item is inserted into a container.
Which two actions should you perform to ensure that the trigger runs? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Register the trigger as a pre-trigger.
B. For each create request, set the access condition in RequestOptions.
C. For each create request, set the consistency level to session in RequestOptions.
D. Append pre to the name of the JavaScript function trigger.
E. For each create request, set the trigger name in RequestOptions.

Answer: A,E

Explanation:
C: When triggers are registered, you can specify the operations that it can run with.
F: When executing, pre-triggers are passed in the RequestOptions object by specifying PreTriggerInclude and then passing the name of the trigger in a List object.

NEW QUESTION # 114
You have an Azure subscription that contains an Azure Cosmos DB for NoSQL account named account1 and a Log Analytics workspace named Workspace1. Workspace 1 stores the logs of account1.
You need to identify which operations used the most request units per second (RU/s) during the last 24 hours.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Explanation

NEW QUESTION # 115
You have an Azure Cosmos DB account named account1 that has a default consistency level of session.
You have an app named App1.
You need to ensure that the read operations of App1 can request either bounded staleness or consistent prefix consistency.
What should you modify for each consistency level? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1 = The request level options
Azure Cosmos DB offers five well-defined consistency levels: strong, bounded staleness, session, consistent prefix and eventual. You can configure the default consistency level on your Azure Cosmos DB account at any time2. The default consistency level applies to all databases and containers under that account1. You can also override the default consistency level for a specific request by using the request options2.
Box 2 = The request level options
To modify the consistency level of a read operation in Azure Cosmos DB, you can use request-level options to override the account's default consistency setting. Therefore, to ensure that the read operations of App1 can request either consistent prefix or session consistency, you need to modify the request-level options for each operation. Reference: - https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

NEW QUESTION # 116
You have an Azure Cosmos DB for NoSQL account that contains a container named container1.
You plan to use container1 as a key-value store that will perform point reads on the item ID and partition key.
You need to define an indexing policy for container! The solution must meet the following requirements:
* Provide the ability to set DefauitTimeToLive for container1.
* Minimize implementation time.
* Minimize costs.
How should you complete the JSON definition of the indexing policy? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 117
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?

A. 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.
B. In a directory container, create a document for each company and a document for each user. Use company ID as the partition key.
C. In a company container, create a document for each company. Embed the users into company documents. Use the company ID as the partition key.
D. 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: C

Explanation:
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 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 ID1.
* It avoids storing redundant data or creating additional containers for users1.
* It allows you to browse by company and browse the users by company with simple queries1.
* It shows a details page for the company and all the related users by fetching a single document1.
* It leverages the benefits of embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.
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 exceed the maximum document size limit of 2 MB2.
* It may not be optimal for scenarios where users need to be associated with more than one company or queried independently from companies2.
* It may not be scalable for companies that have more than 1,000 users, as it could result in hot partitions or throttling2.
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 data2
, or you could use a graph data model that expresses entities and relationships as vertices and edges.

NEW QUESTION # 118
......

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Certification DP-420 Exam Cost, Free DP-420 Practice

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q113-Q118):

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