2026 DP-420: Authoritative Certificate Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Exam

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Microsoft DP-420 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Integrate an Azure Cosmos DB solution5-10%- Implement solutions across services
  • 1. Use Change Feed for denormalization
  • 2. Integrate Azure Functions and Event Hubs
  • 3. Enforce referential integrity
  • 4. Implement Azure AI Search integration
  • 5. Aggregate and archive data
- Enable analytical workloads
  • 1. Enable analytical store
  • 2. Write data back from Spark
  • 3. Configure Azure Cosmos DB Mirroring
  • 4. Query analytical store from Synapse
Topic 2: Optimize an Azure Cosmos DB solution15-20%- Define and implement indexing strategy
  • 1. Implement composite indexes
  • 2. Choose appropriate index types
  • 3. Configure custom indexing policies
  • 4. Optimize index performance
  • 5. Choose read-heavy or write-heavy indexing
- Design and implement change feeds
  • 1. Consume change feeds using SDK
  • 2. Manage change feed instances
  • 3. Develop Azure Functions triggers
  • 4. Implement aggregation persistence
- Optimize query performance
  • 1. Implement integrated cache
  • 2. Adjust indexing policies
  • 3. Retrieve request unit cost
  • 4. Calculate query cost
Topic 3: Design and implement data distribution5-10%- 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 replication strategy
  • 1. Choose when to distribute data
  • 2. Choose a consistency model
  • 3. Perform manual failovers
  • 4. Define automatic failover policies
  • 5. Evaluate consistency model impacts
Topic 4: Maintain an Azure Cosmos DB solution25-30%- Monitor and troubleshoot Azure Cosmos DB solutions
  • 1. Monitor performance and availability
  • 2. Monitor metrics and logs
  • 3. Troubleshoot throughput issues
  • 4. Implement backup and recovery strategies
Topic 5: Design and implement data models35-40%- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
  • 1. Store multiple entity types in the same container
  • 2. Denormalize data across documents
  • 3. Identify partition key, id, and unique keys
  • 4. Reference between documents
  • 5. Store multiple related entities in the same document
  • 6. Identify data and associated access patterns
- Design a data partitioning strategy
  • 1. Choose a partitioning strategy
  • 2. Evaluate cross-partition query cost
  • 3. Implement synthetic partition keys
  • 4. Evaluate throughput and data distribution
  • 5. Plan for transactions with partition keys
  • 6. Choose a partition key
- Plan and implement sizing and scaling
  • 1. Configure throughput
  • 2. Evaluate global distribution costs
  • 3. Evaluate throughput and storage requirements
  • 4. Design resource governance and scaling
  • 5. Choose between serverless, provisioned, and free tier
- Implement data access
  • 1. Use SDKs for CRUD operations
  • 2. Manage transactional batch operations
  • 3. Use consistency and query request options
  • 4. Implement patch operations
  • 5. Implement optimistic concurrency control
  • 6. Implement SQL queries with arrays and aggregation
- Implement client connectivity options
  • 1. Configure client-side threading and logging
  • 2. Handle connection errors
  • 3. Create database connections
  • 4. Implement singleton client
  • 5. Use Azure Cosmos DB emulator
  • 6. Choose gateway or direct connectivity mode
- Implement server-side programming
  • 1. Implement user-defined functions
  • 2. Write and deploy stored procedures
  • 3. Implement triggers

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q154-Q159):

NEW QUESTION # 154
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 a container named container1 in an Azure Cosmos DB Core (SQL) API account.
You need to make the contents of container1 available as reference data for an Azure Stream Analytics job.
Solution: You create an Azure Data Factory pipeline that uses Azure Cosmos DB Core (SQL) API as the input and Azure Blob Storage as the output.
Does this meet the goal?

Answer: B

Explanation:
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.
The following diagram represents the data flow and components involved in the solution:

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


NEW QUESTION # 155
You have an Azure subscription that contains a resource group named RG1. RG1 contains an Azure Cosmos DB for NoSQL account named account1.
You plan to audit changes to the control plane of account1.
You need to ensure that audit events include the details of the security principal that performs each control plane change.
How should you complete the az command? To answer select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 156
You have an Azure Cosmos DB for NoSQL account named accounts1.
You plan to implement the integrated cache for account1.
You need to configure the connectivity mode and the consistency level for requests that target account1. The solution must maximize consistency while using the integrated cache.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 157
You configure Azure Cognitive Search to index a container in an Azure Cosmos DB Core (SQL) API account as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: country
The country field is filterable.
Note: filterable: Indicates whether to enable the field to be referenced in $filter queries. Filterable differs from searchable in how strings are handled. Fields of type Edm.String or Collection(Edm.String) that are filterable do not undergo lexical analysis, so comparisons are for exact matches only.
Box 2: name
The name field is not Retrievable.
Retrievable: Indicates whether the field can be returned in a search result. Set this attribute to false if you want to use a field (for example, margin) as a filter, sorting, or scoring mechanism but do not want the field to be visible to the end user.
Note: searchable: Indicates whether the field is full-text searchable and can be referenced in search queries.
Reference: https://docs.microsoft.com/en-us/rest/api/searchservice/create-index


NEW QUESTION # 158
You have an Apache Spark pool in Azure Synapse Analytics that runs the following Python code in a notebook.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
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 container 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-time 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.


NEW QUESTION # 159
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