DP-420 Online Practice Questions and Answers

Correct Answer:

Box 1: incorrect connection URLs

400 Bad Request: Returned when there is an error in the request URI, headers, or body. The response body will contain an error message explaining what the specific problem is.

The HyperText Transfer Protocol (HTTP) 400 Bad Request response status code indicates that the server cannot or will not process the request due to something that is perceived to be a client error (for example, malformed request syntax,

invalid request message framing, or deceptive request routing).

Box 2: 6 thousand

201 Created: Success on PUT or POST. Object created or updated successfully.

Note:

200 OK: Success on GET, PUT, or POST. Returned for a successful response.

404 Not Found: Returned when a resource does not exist on the server. If you are managing or querying an index, check the syntax and verify the index name is specified correctly.

Reference:

https://docs.microsoft.com/en-us/rest/api/searchservice/http-status-codes

Correct Answer: B

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:

1.

The size and shape of your data

2.

The frequency and complexity of your queries

3.

The latency and throughput requirements of your application

4.

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:

1.

It stores orders and order items as self-contained documents that can be easily retrieved by order ID1.

2.

It avoids storing redundant data or creating additional containers for order items1.

3.

It allows you to view the order history of a customer with simple queries1.

4.

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.

Correct Answer: BC

Correct Answer: CEF

Correct Answer: A

According to the Azure Cosmos DB documentation12, the change feed is a persistent record of changes to a container in the order they occur. The change feed outputs the sorted list of documents that were changed in the order in which

they were modified.

The Azure function that has a trigger on the change feed receives all the properties of the updated items2. The change feed does not include the original items or only the changed properties. The change feed also includes some system-

defined properties such as _ts (the last modified timestamp) and _lsn (the logical sequence number)3.

Therefore, the correct answer is "all the properties of the updated items"

Correct Answer: B

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.

Correct Answer: CDF

C: Avro is binary format, while JSON is text.

F: Kafka Connect for Azure Cosmos DB is a connector to read from and write data to Azure Cosmos DB. The Azure Cosmos DB sink connector allows you to export data from Apache Kafka topics to an Azure Cosmos DB database. The

connector polls data from Kafka to write to containers in the database based on the topics subscription.

D: Create the Azure Cosmos DB sink connector in Kafka Connect. The following JSON body defines config for the sink connector.

Extract:

"connector.class": "com.azure.cosmos.kafka.connect.sink.CosmosDBSinkConnector",

"key.converter": "org.apache.kafka.connect.json.AvroConverter"

"connect.cosmos.containers.topicmap": "hotels#kafka"

Incorrect Answers:

B: JSON is plain text.

Note, full example:

{ "name": "cosmosdb-sink-connector", "config": {

"connector.class": "com.azure.cosmos.kafka.connect.sink.CosmosDBSinkConnector",

"tasks.max": "1",

"topics": [

"hotels"

],

"value.converter": "org.apache.kafka.connect.json.AvroConverter",

"value.converter.schemas.enable": "false",

"key.converter": "org.apache.kafka.connect.json.AvroConverter",

"key.converter.schemas.enable": "false",

"connect.cosmos.connection.endpoint": "https://.documents.azure.com:443/",

"connect.cosmos.master.key": "",

"connect.cosmos.databasename": "kafkaconnect",

"connect.cosmos.containers.topicmap": "hotels#kafka"

} }

Reference:

https://docs.microsoft.com/en-us/azure/cosmos-db/sql/kafka-connector-sink

https://www.confluent.io/blog/kafka-connect-deep-dive-converters-serialization-explained/

Correct Answer: C

Batch size: An integer that represents how many objects are being written to Cosmos DB collection in each batch. Usually, starting with the default batch size is sufficient. To further tune this value, note:

Cosmos DB limits single request's size to 2MB. The formula is "Request Size = Single Document Size * Batch Size". If you hit error saying "Request size is too large", reduce the batch size value.

The larger the batch size, the better throughput the service can achieve, while make sure you allocate enough RUs to empower your workload.

Incorrect Answers:

A: Throughput: Set an optional value for the number of RUs you'd like to apply to your CosmosDB collection for each execution of this data flow. Minimum is 400.

B: Write throughput budget: An integer that represents the RUs you want to allocate for this Data Flow write operation, out of the total throughput allocated to the collection.

D: Collection action: Determines whether to recreate the destination collection prior to writing.

None: No action will be done to the collection. Recreate: The collection will get dropped and recreated

Reference: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db

Correct Answer: B

You can facilitate aggregate data by using Change Feed and Azure Functions, and then use it for reporting.

Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/change-feed

Correct Answer: B

Instead create an Azure function that uses Azure Cosmos DB Core (SQL) API change feed as a trigger and Azure event hub as the output.

The Azure Cosmos DB change feed is a mechanism to get a continuous and incremental feed of records from an Azure Cosmos container as those records are being created or modified. Change feed support works by listening to container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.

Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/changefeed-ecommerce-solution