Use this quick start guide to collect all the information about Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-420 Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCS Azure Cosmos DB Developer certification exam.
The Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure Cosmos DB Developer Specialty exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCS Azure Cosmos DB Developer.
Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Exam Summary:
| Exam Name | Microsoft Certified - Azure Cosmos DB Developer Specialty |
| Exam Code | DP-420 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-420T00-A: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB |
| Schedule Exam | Pearson VUE |
| Sample Questions | Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions |
| Practice Exam | Microsoft DP-420 Certification Practice Exam |
Microsoft DP-420 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Design and Implement Data Models (35-40%) |
|
| Design and implement a non-relational data model for Azure Cosmos DB for NoSQL |
- Develop a design by storing multiple entity types in the same container - Develop a design by storing multiple related entities in the same document - Develop a model that denormalizes data across documents - Develop a design by referencing between documents - Identify primary and unique keys - Identify data and associated access patterns - Specify a default time to live (TTL) on a container for a transactional store - Develop a design for versioning documents - Develop a design for document schema versioning |
| Design a data partitioning strategy for Azure Cosmos DB for NoSQL |
- Choose a partitioning strategy based on a specific workload - Choose a partition key - Plan for transactions when choosing a partition key - Evaluate the cost of using a cross-partition query - Calculate and evaluate data distribution based on partition key selection - Calculate and evaluate throughput distribution based on partition key selection - Construct and implement a synthetic partition key - Design and implement a hierarchical partition key - Design partitioning for workloads that require multiple partition keys |
| Plan and implement sizing and scaling for a database created with Azure Cosmos DB |
- Evaluate the throughput and data storage requirements for a specific workload - Choose between serverless, provisioned and free models - Choose when to use database-level provisioned throughput - Design for granular scale units and resource governance - Evaluate the cost of the global distribution of data - Configure throughput for Azure Cosmos DB by using the Azure portal |
| Implement client connectivity options in the Azure Cosmos DB SDK |
- Choose a connectivity mode (gateway versus direct) - Implement a connectivity mode - Create a connection to a database - Enable offline development by using the Azure Cosmos DB emulator - Handle connection errors - Implement a singleton for the client - Specify a region for global distribution - Configure client-side threading and parallelism options - Enable SDK logging |
| Implement data access by using the SQL language for Azure Cosmos DB for NoSQL |
- Implement queries that use arrays, nested objects, aggregation, and ordering - Implement a correlated subquery - Implement queries that use array and type-checking functions - Implement queries that use mathematical, string, and date functions - Implement queries based on variable data |
| Implement data access by using Azure Cosmos DB for NoSQL SDKs |
- Choose when to use a point operation versus a query operation - Implement a point operation that creates, updates, and deletes documents - Implement an update by using a patch operation - Manage multi-document transactions using SDK Transactional Batch - Perform a multi-document load using Bulk Support in the SDK - Implement optimistic concurrency control using ETags - Override default consistency by using query request options - Implement session consistency by using session tokens - Implement a query operation that includes pagination - Implement a query operation by using a continuation token - Handle transient errors and 429s - Specify TTL for a document - Retrieve and use query metrics |
| Implement server-side programming in Azure Cosmos DB for NoSQL by using JavaScript |
- Write, deploy, and call a stored procedure - Design stored procedures to work with multiple documents transactionally - Implement and call triggers - Implement a user-defined function |
Design and Implement Data Distribution (5-10%) |
|
| Design and implement a replication strategy for Azure Cosmos DB |
- Choose when to distribute data - Define automatic failover policies for regional failure for Azure Cosmos DB for NoSQL - Perform manual failovers to move single master write regions - Choose a consistency model - Identify use cases for different consistency models - Evaluate the impact of consistency model choices on availability and associated request unit (RU) cost - Evaluate the impact of consistency model choices on performance and latency - Specify application connections to replicated data |
| Design and implement multi-region write |
- Choose when to use multi-region write - Implement multi-region write - Implement a custom conflict resolution policy for Azure Cosmos DB for NoSQL |
Integrate an Azure Cosmos DB Solution (5-10%) |
|
| Enable Azure Cosmos DB analytical workloads |
- Enable Azure Synapse Link - Choose between Azure Synapse Link and Spark Connector - Enable the analytical store on a container - Implement custom partitioning in Azure Synapse Link - Enable a connection to an analytical store and query from Azure Synapse Spark or Azure Synapse SQL - Perform a query against the transactional store from Spark - Write data back to the transactional store from Spark - Implement Change Data Capture in the Azure Cosmos DB analytical store - Implement time travel in Azure Synapse Link for Azure Cosmos DB |
| Implement solutions across services |
- Integrate events with other applications by using Azure Functions and Azure Event Hubs - Denormalize data by using Change Feed and Azure Functions - Enforce referential integrity by using Change Feed and Azure Functions - Aggregate data by using Change Feed and Azure Functions, including reporting - Archive data by using Change Feed and Azure Functions - Implement Azure AI Search for an Azure Cosmos DB solution |
Optimize an Azure Cosmos DB Solution (15-20%) |
|
| Optimize query performance when using the API for Azure Cosmos DB for NoSQL |
- Adjust indexes on the database - Calculate the cost of the query - Retrieve request unit cost of a point operation or query - Implement Azure Cosmos DB integrated cache |
| Design and implement change feeds for Azure Cosmos DB for NoSQL |
- Develop an Azure Functions trigger to process a change feed - Consume a change feed from within an application by using the SDK - Manage the number of change feed instances by using the change feed estimator - Implement denormalization by using a change feed - Implement referential enforcement by using a change feed - Implement aggregation persistence by using a change feed - Implement data archiving by using a change feed |
| Define and implement an indexing strategy for Azure Cosmos DB for NoSQL |
- Choose when to use a read-heavy versus write-heavy index strategy - Choose an appropriate index type - Configure a custom indexing policy by using the Azure portal - Implement a composite index - Optimize index performance |
Maintain an Azure Cosmos DB Solution (25-30%) |
|
| Monitor and troubleshoot an Azure Cosmos DB solution |
- Evaluate response status code and failure metrics - Monitor metrics for normalized throughput usage by using Azure Monitor - Monitor server-side latency metrics by using Azure Monitor - Monitor data replication in relation to latency and availability - Configure Azure Monitor alerts for Azure Cosmos DB - Implement and query Azure Cosmos DB logs - Monitor throughput across partitions - Monitor distribution of data across partitions - Monitor security by using logging and auditing |
| Implement backup and restore for an Azure Cosmos DB solution |
- Choose between periodic and continuous backup - Configure periodic backup - Configure continuous backup and recovery - Locate a recovery point for a point-in-time recovery - Recover a database or container from a recovery point |
| Implement security for an Azure Cosmos DB solution |
- Choose between service-managed and customer-managed encryption keys - Configure network-level access control for Azure Cosmos DB - Configure data encryption for Azure Cosmos DB - Manage control plane access to Azure Cosmos DB by using Azure role-based access control (RBAC) - Manage data plane access to Azure Cosmos DB by using keys - Manage data plane access to Azure Cosmos DB by using Microsoft Entra ID - Configure Cross-Origin Resource Sharing (CORS) settings - Manage account keys by using Azure Key Vault - Implement customer-managed keys for encryption - Implement Always Encrypted |
| Implement data movement for an Azure Cosmos DB solution |
- Choose a data movement strategy - Move data by using client SDK bulk operations - Move data by using Azure Data Factory and Azure Synapse pipelines - Move data by using a Kafka connector - Move data by using Azure Stream Analytics - Move data by using the Azure Cosmos DB Spark Connector - Configure Azure Cosmos DB as a custom endpoint for an Azure IoT Hub |
| Implement a DevOps process for an Azure Cosmos DB solution |
- Choose when to use declarative versus imperative operations - Provision and manage Azure Cosmos DB resources by using Azure Resource Manager templates - Migrate between standard and autoscale throughput by using PowerShell or Azure CLI - Initiate a regional failover by using PowerShell or Azure CLI - Maintain indexing policies in production by using Azure Resource Manager templates |
To ensure success in Microsoft MCS Azure Cosmos DB Developer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420) exam.