DP-420 Practice Exam Questions with Answers Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Certification

Use a partition key with a random suffix. Distribute the workload more evenly is to append a random number at the end of the partition key value. When you distribute items in this way, you can perform parallel write operations across partitions.

Box 1 = Range Azure Cosmos DB supports three types of indexes: range, spatial and composite. For the query you provided, which is an equality query on a single property, the best type of index to use is range. Range index is based on an ordered tree-like structure and it is used for equality queries, range queries and checking for the presence of a property1. Range index also supports any string or number2.

Box 2 = Composite

Azure Cosmos DB supports three types of indexes: range, spatial and composite. For the query you provided, which is an order by query on two properties, the best type of index to use is composite. Composite index is used for optimizing order by queries on multiple properties1. Composite index allows you to specify a list of property paths and sort orders that are used for ordering items2.

Box 3 = spatial

Azure Cosmos DB supports three types of indexes: range, spatial and composite. For the query you provided, which is a spatial query on a point property, the best type of index to use is spatial. Spatial index is used for querying items based on their location or proximity to a given point1. Spatial index supports point, polygon and linestring data types2.

New and updated orders will be added to contoso-erp.orders: Yes

The code performs bulk data ingestion from contoso-app: No

Both contoso-app and contoso-erp have Analytics store enabled: Yes

The code uses the spark.readStream method to read data from a container named orders in a database named contoso-app. The data is then filtered by a condition and written to another container named orders in a database named contoso-erp using the spark.writeStream method. The write mode is set to “append”, which means that new and updated orders will be added to the destination container1.

The code does not perform bulk data ingestion from contoso-app, but rather stream processing. Bulk data ingestion is a process of loading large amounts of data into a data store in batches. Stream processing is a process of continuously processing data as it arrives in real-time2.

Both contoso-app and contoso-erp have Analytics store enabled, because they are both accessed by Spark pools using the spark.cosmos.oltp method. This method requires that the containers have Analytics store enabled, which is a feature that allows Spark pools to query data stored in Azure Cosmos DB containers using SQL APIs3.

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.

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.

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.

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.

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.

Box 1: Yes

Need to join readings and devices.

Box 2: No

Only readings is required. All required fields are in readings.

Box 3: No

Only devices is required. All required fields are in devices.