Microsoft DP-420 Practice Test - Questions Answers, Page 5

For your scenario, to ensure that App1 can use the integrated cache, you should perform these two actions:

Change the account endpoint to https://account1.sqlx.cosmos.azure.com. This is the dedicatedgateway endpoint that you need to use to connect to your Azure Cosmos DB account and leveragethe integrated cache. The standard gateway endpoint (https://account1.documents.azure.com) willnot use the integrated cache2.

Change the consistency level of requests to session. This is the highest consistency level that is supported by the integrated cache. If you use a higher consistency level (such as strong or bounded staleness), your requests will bypass the integrated cache and go directly to the backend containers

The cost of a point read for a 1 KB item is 1 RU. The cost of other operations depends on factors such as item size, indexing policy, consistency level, and query complexity1. To minimize the consumption of RUs, you can optimize these factors according to your application needs.

For your scenario, one possible way to minimize the consumption of RUs associated to the reads by App1 is to change the consistency level of read requests to consistent prefix. Consistent prefix is a lower consistency level than session, which is the default consistency level for Azure Cosmos DB. Lower consistency levels consume fewer RUs than higher consistency levels2. Consistent prefix guarantees that reads never see out-of-order writes and that monotonic reads are preserved1. This may be suitable for your application if you can tolerate some eventual consistency.

Azure Cosmos DB offers two different capacity modes: provisioned throughput and serverless1.

Provisioned throughput mode allows you to configure a certain amount of throughput (expressed in Request Units per second or RU/s) that is provisioned on your databases and containers. You get billed for the amount of throughput you've provisioned, regardless of how many RUs were consumed1. Serverless mode allows you to run your database operations without having to configure any previously provisioned capacity. You get billed for the number of RUs that were consumed by your database operations and the storage consumed by your data1.

To create a database that minimizes costs, you should consider the following factors:

The read and write volumes of your containers

The predictability and variability of your traffic

The latency and throughput requirements of your application

The geo-distribution and availability needs of your data Based on these factors, one possible option that you could choose is B. Create a provisioned throughput account. Set the throughput for coll1 to Autoscale. Set the throughput for coll2 and coll3 to Manual.

This option has the following advantages:

It allows you to handle unpredictable read and write volumes for coll1 by using Autoscale, which automatically adjusts the provisioned throughput based on the current load1.

It allows you to handle predictable read and write volumes for coll2 and coll3 by using Manual, which lets you specify a fixed amount of provisioned throughput that meets your performance needs1.

It allows you to optimize your costs by paying only for the throughput you need for each container1.

It allows you to enable geo-distribution for your account if you need to replicate your data across multiple regions1.

This option also has some limitations, such as:

It may not be suitable for scenarios where all containers have intermittent or bursty traffic that is hard to forecast or has a low average-to-peak ratio1.

It may not be optimal for scenarios where all containers have low or sporadic traffic that does not justify provisioned capacity1.

It may not support availability zones or multi-master replication for your account1.

Depending on your specific use case and requirements, you may need to choose a different option. For example, you could use a serverless account if all containers have low or sporadic traffic that does not require predictable performance or geo-distribution1. Alternatively, you could use a provisioned throughput account with Manual for all containers if all containers have stable and consistent traffic that requires predictable performance or geo-distribution1.

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.

Based on the characteristics of the application and the provided document structure, the most suitable partition key value for coll1 in the given scenario would be the customerId, Option B.

The application frequently creates new orders by different customers and customers often view their past order history. Using customerId as the partition key would ensure that all orders associated with a particular customer are stored in the same partition. This enables efficient querying of past order history for a specific customer and reduces cross-partition queries, resulting in lower costs and improved performance. a partition key is a JSON property (or path) within your documents that is used by Azure Cosmos DB to distribute data among multiple partitions3. A partition key should have a high cardinality, which means it should have many distinct values, such as hundreds or thousands1. A partition key should also align with the most common query patterns of your application, so that you can efficiently retrieve data by using the partition key value1.

Based on these criteria, one possible partition key that you could use for coll1 is B. customerId.

This partition key has the following advantages:

It has a high cardinality, as each customer will have a unique ID3.

It aligns with the query patterns of the application, as customers will often view their past order history3.

It minimizes costs, as it reduces the number of cross-partition queries and optimizes the storage and throughput utilization1.

This partition key also has some limitations, such as:

It may not be optimal for scenarios where orders need to be queried independently from customers or aggregated by date or other criteria3.

It may result in hot partitions or throttling if some customers create orders more frequently than others or have more data than others1.

It may not support transactions across multiple customers, as transactions are scoped to a single logical partition2.

Depending on your specific use case and requirements, you may need to adjust this partition key or choose a different one. For example, you could use a synthetic partition key that concatenates multiple properties of an item2, or you could use a partition key with a random or pre-calculated suffix to distribute the workload more evenly2.

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.

This is because the write concern is mapped to the default consistency level configured on your Azure Cosmos DB account, which is strong in this case. Strong consistency ensures that every write operation is synchronously committed to every region associated with your Azure Cosmos DB account. The eventual consistency level that the application uses only applies to the read operations.

Eventual consistency offers higher availability and better performance, but it does not guarantee the order or latency of the reads.

Setting ConflictResolutionMode to Custom and using the default settings for the policy will notensure that conflicts are sent to the conflict feed. You need to define a custom storedprocedure using the 'conflictingItems' parameter to handle conflicts properly.