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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB |
| Exam Number: | DP-420 |
| Exam Duration: | 100-120 |
| Exam Format: | Drag and drop, Multiple choice, Case study, Hot area, Lab |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: Azure Cosmos DB Developer Specialty |
| Passing Score: | 700/1000 |
| Available Languages: | English, Chinese (Simplified), Japanese, Korean |
| Exam Price: | $165 USD |
| Certificate Validity Period: | 1 year |
| Sample Questions: | Microsoft DP-420 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisite exams are required. Candidates should have experience developing Azure solutions and working with Azure Cosmos DB. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-420/ |
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The Microsoft DP-420 exam DP-420 is aimed at professionals who have experience in designing and implementing Cosmos DB solutions. DP-420 exam is intended for those who are looking to enhance their knowledge and skills in cloud-native application development by leveraging Microsoft Azure Cosmos DB. DP-420 Exam covers various topics related to Cosmos DB, including data modeling, partitioning, indexing, querying, and performance tuning.
NEW QUESTION # 217
You have an Azure Cosmos DB for NoSQL account named account1.
In account1, you plan to create a container named container1 that will contain data continuously collected from self-driving cars. The items in container1 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?
Answer: A
Explanation:
For an Azure Cosmos DB for NoSQL container storing continuous self-driving car data, the optimal partition strategy is to use the requested synthetic partition key constructed by concatenating the current date and a hash of the Vehicle-Identification-Number (VIN).
Why this strategy is optimal:
Maximizes Read Performance:
Since your primary access pattern targets a specific car on a given day, this synthetic key ensures that all relevant data for that query resides in a single logical partition.
This allows the database to perform efficient in-partition queries rather than expensive and slow cross-partition scans.
Maximizes Write Performance:
Including a hash of the VIN ensures high cardinality, which prevents "hot partitions".
Even if many cars are reporting data simultaneously, the hash-based distribution spreads these writes across multiple physical partitions.
Minimizes Costs:
By avoiding cross-partition queries, you significantly reduce Request Unit (RU) consumption, as each physical partition scan adds overhead (typically 2-3 RUs per partition).
The 20-GB logical partition limit is less likely to be exceeded because data for a single car is scoped to a single day rather than its entire history.
Reference:
https://www.dclessons.com/azure-cosmos-db
NEW QUESTION # 218
You need to configure an Apache Kafka instance to ingest data from an Azure Cosmos DB Core (SQL) API account. The data from a container named telemetry must be added to a Kafka topic named iot. The solution must store the data in a compact binary format.
Which three configuration items should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
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": "Error! Hyperlink reference not valid.",
"connect.cosmos.master.key": "<cosmosdbprimarykey>",
"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/
NEW QUESTION # 219
You need to recommend indexes for con-product and con-productVendor. The solution must meet the product catalog requirements and the business requirements.
Which type of index should you recommend for each container? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 220
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?
Answer: A
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.
NEW QUESTION # 221
You have an Azure Cosmos DB Core (SQL) API account used by an application named App1.
You open the Insights pane for the account and see the following chart.
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 222
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