Microsoft DP-420 Questions & Answers - in .pdf
- Total Q&A: 206
- Update: Oct 19, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: DP-420
- Exam Name: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB
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Skills measured
- Maintain an Azure Cosmos DB solution (25–30%)
- The English language version of this exam will be updated on August 4, 2022. Please download the exam skills outline below to see what’s changing.
- Design and implement data models (35–40%)
- Design and implement data distribution (5–10%)
- Integrate an Azure Cosmos DB solution (5–10%)
- Optimize an Azure Cosmos DB solution (15–20%)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-420?WT.mc_id=Azure_blog-wwl
Exam DP-420: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB
Candidates for this exam must have solid knowledge and experience developing apps for Azure and working with Azure Cosmos DB database technologies. They should be proficient at developing applications by using the Core (SQL) API and SDKs, writing efficient queries and creating appropriate index policies, provisioning and managing resources in Azure, and creating server-side objects with JavaScript. They should be able to interpret JSON, read C# or Java code, and use PowerShell.
The English language version of this exam will be updated on August 4, 2022. Please download the skills measured document below to see what’s changing. If a localized version of this exam is available, it will be updated approximately eight weeks after this date.
Passing score: 700. Learn more about exam scores.
Part of the requirements for: Microsoft Certified: Azure Cosmos DB Developer Specialty
Download exam skills outline
Microsoft DP-420 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Integrate an Azure Cosmos DB solution | 5-10% | - Implement solutions across services
- 1. Integrate Azure Functions and Event Hubs
- 2. Aggregate and archive data
- 3. Enforce referential integrity
- 4. Implement Azure AI Search integration
- 5. Use Change Feed for denormalization
- Enable analytical workloads
- 1. Enable analytical store
- 2. Configure Azure Cosmos DB Mirroring
- 3. Write data back from Spark
- 4. Query analytical store from Synapse
|
| Maintain an Azure Cosmos DB solution | 25-30% | - Monitor and troubleshoot Azure Cosmos DB solutions
- 1. Troubleshoot throughput issues
- 2. Implement backup and recovery strategies
- 3. Monitor metrics and logs
- 4. Monitor performance and availability
|
| Design and implement data distribution | 5-10% | - Design and implement replication strategy
- 1. Define automatic failover policies
- 2. Choose when to distribute data
- 3. Evaluate consistency model impacts
- 4. Choose a consistency model
- 5. Perform manual failovers
- Design and implement multi-region writes
- 1. Implement conflict resolution policies
- 2. Choose when to use multi-region writes
- 3. Implement multi-region writes
|
| Design and implement data models | 35-40% | - Implement server-side programming
- 1. Implement user-defined functions
- 2. Write and deploy stored procedures
- 3. Implement triggers
- Design a data partitioning strategy
- 1. Plan for transactions with partition keys
- 2. Choose a partitioning strategy
- 3. Evaluate cross-partition query cost
- 4. Implement synthetic partition keys
- 5. Choose a partition key
- 6. Evaluate throughput and data distribution
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
- 1. Store multiple entity types in the same container
- 2. Identify partition key, id, and unique keys
- 3. Identify data and associated access patterns
- 4. Denormalize data across documents
- 5. Store multiple related entities in the same document
- 6. Reference between documents
- Implement data access
- 1. Implement SQL queries with arrays and aggregation
- 2. Implement patch operations
- 3. Implement optimistic concurrency control
- 4. Use consistency and query request options
- 5. Use SDKs for CRUD operations
- 6. Manage transactional batch operations
- Plan and implement sizing and scaling
- 1. Evaluate global distribution costs
- 2. Evaluate throughput and storage requirements
- 3. Design resource governance and scaling
- 4. Configure throughput
- 5. Choose between serverless, provisioned, and free tier
- Implement client connectivity options
- 1. Choose gateway or direct connectivity mode
- 2. Create database connections
- 3. Handle connection errors
- 4. Implement singleton client
- 5. Use Azure Cosmos DB emulator
- 6. Configure client-side threading and logging
|
| Optimize an Azure Cosmos DB solution | 15-20% | - Define and implement indexing strategy
- 1. Choose read-heavy or write-heavy indexing
- 2. Implement composite indexes
- 3. Optimize index performance
- 4. Choose appropriate index types
- 5. Configure custom indexing policies
- Design and implement change feeds
- 1. Develop Azure Functions triggers
- 2. Manage change feed instances
- 3. Consume change feeds using SDK
- 4. Implement aggregation persistence
- Optimize query performance
- 1. Implement integrated cache
- 2. Retrieve request unit cost
- 3. Calculate query cost
- 4. Adjust indexing policies
|
Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions:
Question #1
You have a multi-region Azure Cosmos DB account named account1 that has a default consistency level of strong.
You have an app named App1 that is configured to request a consistency level of session.
How will the read and write operations of App1 be handled? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question #2
You have an Azure Cosmos DB for NoSQL database that hosts a container named Contained. Contained stores product details. You have a query named Query1 that contains the following statements.
You need to optimize the performance of Query1 by using custom indexing. Which set of index components will optimize Query1?
A. ListPrice ASC, ProductName ASC, ProductGroup ASC, Color ASC
B. ListPrice ASC, ProductName ASC
C. ListPrice DESC, ProductName ASC, ProductGroup ASC, Color ASC
D. ProductName DESC, ListPrice ASC
Question #3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cosmos DB Core (SQL) API account named account 1 that uses autoscale throughput.
You need to run an Azure function when the normalized request units per second for a container in account1 exceeds a specific value.
Solution: You configure an application to use the change feed processor to read the change feed and you configure the application to trigger the function.
Does this meet the goal?
A. No
B. Yes
Question #4
You have an Azure Cosmos DB database that hosts a container named container1.
You need to ensure that the items stored in contained will NOT expire unless their TTL value is set explicitly.
What should you do?
A. Set the DefaultTineToLive property to -l.
B. Remove the DefaultTiiwroLive property.
C. Set the DefaultiiwioLive property to null.
D. Set the DefaulttimtoLive property to e.
Question #5
You have an Azure Cosmos DB for NoSQL account that uses the default consistency level. How can the consistency level be modified as part of a query request?
A. stronger consistency for write operations
B. weaker consistency for read operations
C. stronger consistency for read operations
D. weaker consistency for write operations
Solutions:
Question #1
Correct Answer: Only visible for members | Question #2
Correct Answer: D | Question #3
Correct Answer: A | Question #4
Correct Answer: A | Question #5
Correct Answer: B |
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