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Microsoft DP-420 (Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB) Exam is a certification exam that validates the skills and knowledge of developers in designing and implementing cloud-native applications using Microsoft Azure Cosmos DB. DP-420 Exam is intended for individuals who have experience in developing and deploying applications on Microsoft Azure and are looking to enhance their skills in designing and implementing cloud-native applications using Cosmos DB.
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Microsoft DP-420 exam focuses on designing and implementing cloud-native applications using Microsoft Azure Cosmos DB. DP-420 exam is ideal for professionals who are looking to gain a deeper understanding of Cosmos DB and its capabilities. Cosmos DB is a globally distributed, multi-model database service that is designed for mission-critical applications. It provides seamless scaling, high availability, and low latency for data access, making it a popular choice for cloud-native applications.
Microsoft DP-420 certification exam consists of multiple-choice questions and is available in several languages. DP-420 exam can be taken online or at a certified testing center. To prepare for the exam, Microsoft recommends that candidates have a strong understanding of Azure Cosmos DB concepts, data modeling, partitioning, indexing, querying, and scaling. Candidates can also take advantage of Microsoft's official training courses and practice exams to help them prepare for the exam. Upon passing the Microsoft DP-420 Certification Exam, candidates will receive a digital badge and certificate that can be shared on social media and added to their resume to showcase their expertise in designing and implementing cloud-native applications using Azure Cosmos DB.
NEW QUESTION # 56
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: B
Explanation:
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 users .
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 # 57
You have an Azure Cosmos DB for NoSQL account that frequently receives the same three queries.
You need to configure indexing to minimize RUs consumed by the queries.
Which type of index should you use for each query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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 QUESTION # 58
You have an Azure Cosmos DB for NoSQL container. The container contains items that have the following properties.
You need to protect the data stored in the container by using Always Encrypted. For each property, you must use the strongest type of encryption and ensure that queries execute properly.
What is the strongest type of encryption that you can apply to each property? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1 =Randomized
Box 2 = Deterministic
Always Encrypted for Azure Cosmos DB supports two types of encryption: deterministic and randomized1.
Deterministic encryption always produces the same encrypted value for any given plain text value.
Randomized encryption produces a different encrypted value for the same plain text value.
For dateOfBirth, randomized encryption is the strongest type of encryption because it provides better protection against statistical analysis and brute-force attacks. Deterministic encryption would not be suitable for dateOfBirth because it could reveal patterns or allow equality comparisons1.
For healthStatus, deterministic encryption is the strongest type of encryption because it allows queries to perform equality comparisons and filters on the encrypted property. Randomized encryption would not be suitable for healthStatus because it would prevent any queries on the encrypted property1.
NEW QUESTION # 59
You have an Azure Cosmos DB Core (SQL) API account that is used by 10 web apps.
You need to analyze the data stored in the account by using Apache Spark to create machine learning models.
The solution must NOT affect the performance of the web apps.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Explore analytical store with Apache Spark
1. Navigate to the Data hub.
2. Select the Linked tab (1), expand the Azure Cosmos DB group (if you don't see this, select the Refresh button above), then expand the WoodgroveCosmosDb account (2). Right-click on the transactions container (3), select New notebook (4), then select Load to DataFrame (5).
3. In the generated code within Cell 1 (3), notice that the spark.read format is set to cosmos.olap.
This instructs Synapse Link to use the container's analytical store. If we wanted to connect to the transactional store, like to read from the change feed or write to the container, we'd use cosmos.oltp instead.
NEW QUESTION # 60
Drag and Drop Question
You have an Azure Cosmos DB for NoSQL database that hosts a container named Container1.
Container1 stores customer documents that have the following structure.
You need to retrieve the city of residence and the age of each customer who is older than 40.
Which three statements should you run in sequence to build the query? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 61
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