MICROSOFTMicrosoft Certified:Azure Cosmos DB Developer Specialty
The Microsoft Certified: Azure Cosmos DB Developer Specialty certification validates your expertise in designing, implementing, and monitoring cloud-native applications using Azure Cosmos DB. It is ideal for developers responsible for data management and integration with Azure services. Achieving this certification demonstrates your ability to optimize and maintain solutions that meet performance and security requirements.
464 practice questions · Updated 2026-07-21
5Domains
17Objectives
143Concepts
464Questions
DP-420 Curriculum
Every domain, objective, and concept the DP-420 exam measures.
- MultipleEntityTypesInContainerDesign a data model that stores multiple entity types within a single Azure Cosmos DB container.
- RelatedEntitiesInDocumentCreate a data model that stores multiple related entities within the same document in Azure Cosmos DB.
- DenormalizeDataAcrossDocumentsDevelop a data model that denormalizes data across multiple documents to optimize read performance.
- DocumentReferencingImplement a design that uses references between documents to maintain relationships in a non-relational data model.
- PartitionKeyIdentificationIdentify and select an appropriate partition key for data distribution and scalability in Azure Cosmos DB.
- IdAndUniqueKeysDetermine the id and unique keys necessary for ensuring data uniqueness and integrity in Azure Cosmos DB.
- AccessPatternsIdentificationAnalyze and identify data access patterns to inform the design of a non-relational data model.
- DefaultTTLSpecificationSpecify a default time to live (TTL) for a container to manage data lifecycle in a transactional store.
- DocumentVersioningDesignDesign a strategy for versioning documents to manage changes over time in Azure Cosmos DB.
- SchemaVersioningDesignDevelop a design for document schema versioning to handle schema changes in a non-relational data model.
- Partitioning Strategy SelectionIdentify the appropriate partitioning strategy for a specific workload in Azure Cosmos DB.
- Partition Key SelectionDetermine the most suitable partition key for a given dataset and workload.
- Transaction Planning with Partition KeysPlan for efficient transactions by selecting an optimal partition key.
- Cross-Partition Query Cost EvaluationAssess the cost implications of executing cross-partition queries.
- Data Distribution EvaluationAnalyze data distribution patterns based on the chosen partition key.
- Throughput Distribution EvaluationEvaluate how the selection of a partition key affects throughput distribution.
- Synthetic Partition Key ConstructionCreate and implement a synthetic partition key to optimize data distribution.
- Hierarchical Partition Key DesignDesign and implement a hierarchical partition key structure for complex datasets.
- Multiple Partition Key DesignDevelop partitioning strategies for workloads requiring multiple partition keys.
- Evaluate Throughput RequirementsAssess the throughput needs for a specific workload in Azure Cosmos DB.
- Evaluate Data Storage RequirementsDetermine the data storage requirements for a specific workload in Azure Cosmos DB.
- Serverless vs Provisioned ThroughputIdentify the differences between serverless and provisioned throughput in Azure Cosmos DB and decide which to use.
- Free Tier ApplicabilityDetermine when the Azure Cosmos DB free tier is applicable for a workload.
- Database-Level Provisioned ThroughputDecide when to use database-level provisioned throughput in Azure Cosmos DB.
- Granular Scale Units DesignDesign Azure Cosmos DB for granular scale units to optimize resource governance.
- Resource GovernanceImplement resource governance strategies in Azure Cosmos DB.
- Cost Evaluation of Global DistributionEvaluate the cost implications of globally distributing data in Azure Cosmos DB.
- Configure Throughput via Azure PortalSet up and adjust throughput settings for Azure Cosmos DB using the Azure portal.
- Connectivity ModesUnderstand the differences between gateway and direct connectivity modes in Azure Cosmos DB.
- Implement Connectivity ModeConfigure and implement the chosen connectivity mode in the Azure Cosmos DB SDK.
- Database ConnectionEstablish a connection to an Azure Cosmos DB database using the SDK.
- Azure Cosmos DB EmulatorSet up and use the Azure Cosmos DB emulator for offline development and testing.
- Connection Error HandlingImplement strategies to handle connection errors in Azure Cosmos DB applications.
- Singleton Client ImplementationCreate a singleton pattern for the Azure Cosmos DB client to optimize resource usage.
- Region SpecificationSpecify a preferred region for global distribution in Azure Cosmos DB to optimize latency and availability.
- Client-Side Threading and ParallelismConfigure threading and parallelism options on the client-side to enhance performance.
- SDK LoggingEnable and configure logging in the Azure Cosmos DB SDK to monitor and troubleshoot operations.
- ArrayQueryingExecute queries that manipulate and retrieve data from arrays in Azure Cosmos DB.
- NestedObjectQueryingConstruct queries to access and manipulate nested objects within documents.
- AggregationFunctionsUtilize aggregation functions to summarize data in Azure Cosmos DB queries.
- OrderingResultsApply ordering to query results to sort data based on specified criteria.
- CorrelatedSubqueriesImplement correlated subqueries to perform complex data retrieval operations.
- ArrayFunctionsUse array functions to manipulate and evaluate arrays within queries.
- TypeCheckingFunctionsApply type-checking functions to ensure data type consistency in queries.
- MathematicalFunctionsIncorporate mathematical functions to perform calculations in queries.
- StringFunctionsUse string functions to manipulate text data within queries.
- DateFunctionsImplement date functions to handle and manipulate date and time data in queries.
- VariableDataQueriesConstruct queries that adapt to variable data inputs for dynamic data retrieval.
- Point vs Query OperationsUnderstand the differences and appropriate use cases for point operations and query operations in Azure Cosmos DB.
- Implement Point OperationsExecute point operations to create, update, and delete items using Azure Cosmos DB for NoSQL SDKs.
- Patch OperationsPerform updates on items using patch operations to modify specific properties without replacing the entire item.
- Transactional BatchManage multi-item transactions using SDK Transactional Batch to ensure atomicity and consistency across multiple operations.
- Bulk SupportPerform multi-item loads efficiently using Bulk Support in the Azure Cosmos DB SDK.
- Optimistic Concurrency ControlImplement optimistic concurrency control using ETags to handle concurrent updates to the same item.
- Override ConsistencyOverride the default consistency level for specific queries using query request options.
- Session ConsistencyImplement session consistency by using session tokens to maintain consistency for a user's session.
- Query PaginationImplement query operations that include pagination to manage large result sets effectively.
- Continuation TokensUse continuation tokens to implement query operations that can resume from where the last query left off.
- Handle Transient ErrorsHandle transient errors and 429 status codes to ensure reliable application performance.
- Specify TTLSpecify a Time-to-Live (TTL) for items to automatically delete them after a certain period.
- Query MetricsRetrieve and utilize query metrics to analyze and optimize query performance in Azure Cosmos DB.
- StoredProcedureBasicsUnderstand the purpose and structure of stored procedures in Azure Cosmos DB for NoSQL.
- WriteStoredProcedureWrite a stored procedure using JavaScript for Azure Cosmos DB for NoSQL.
- DeployStoredProcedureDeploy a stored procedure to Azure Cosmos DB for NoSQL.
- CallStoredProcedureInvoke a stored procedure in Azure Cosmos DB for NoSQL from client applications.
- TransactionalStoredProceduresDesign stored procedures to handle multiple items transactionally within the same logical partition.
- TriggerImplementationImplement triggers in Azure Cosmos DB for NoSQL using JavaScript.
- CallTriggersInvoke triggers in Azure Cosmos DB for NoSQL during data operations.
- UserDefinedFunctionBasicsUnderstand the purpose and structure of user-defined functions in Azure Cosmos DB for NoSQL.
- ImplementUserDefinedFunctionCreate and implement a user-defined function in Azure Cosmos DB for NoSQL.
- Data Distribution TimingDetermine the optimal timing for distributing data in Azure Cosmos DB based on application needs.
- Automatic Failover PoliciesDefine automatic failover policies to handle regional failures in Azure Cosmos DB for NoSQL.
- Manual Failover ExecutionPerform manual failovers to relocate single-write regions in Azure Cosmos DB.
- Consistency Model SelectionChoose an appropriate consistency model for Azure Cosmos DB based on application requirements.
- Consistency Model Use CasesIdentify scenarios where different consistency models are applicable in Azure Cosmos DB.
- Consistency Model Impact on AvailabilityEvaluate how different consistency models affect availability and request unit (RU) costs in Azure Cosmos DB.
- Consistency Model Impact on PerformanceAssess the impact of consistency model choices on performance and latency in Azure Cosmos DB.
- Application Connection SpecificationSpecify how applications should connect to replicated data in Azure Cosmos DB.
- Multi-Region Writes BenefitsUnderstand the benefits of using multi-region writes in Azure Cosmos DB, such as improved availability and latency.
- Multi-Region Writes Use CasesIdentify scenarios where multi-region writes are advantageous, such as global applications requiring low-latency access.
- Enable Multi-Region WritesLearn how to configure Azure Cosmos DB to support multi-region writes.
- Conflict Resolution StrategiesUnderstand the need for conflict resolution in multi-region writes and the strategies available.
- Custom Conflict Resolution PolicyImplement a custom conflict resolution policy in Azure Cosmos DB for NoSQL to handle data conflicts.
- Conflict Resolution ConfigurationConfigure conflict resolution settings in Azure Cosmos DB to ensure data consistency across regions.
- Configure Azure Cosmos DB MirroringConfigure Azure Cosmos DB Mirroring for integration with Microsoft Fabric.
- Evaluate Mirroring vs Spark ConnectorDetermine when to use Azure Cosmos DB Mirroring versus the Azure Cosmos DB Spark connector based on workload requirements.
- Enable Analytical StoreEnable the analytical store on an Azure Cosmos DB container to support analytical workloads.
- Connect and Query Analytical StoreEstablish a connection to the analytical store and execute queries using Azure Synapse Spark or Azure Synapse SQL serverless.
- Query Transactional Store from SparkPerform queries against the Azure Cosmos DB transactional store using Spark.
- Write Data to Transactional Store from SparkWrite data back to the Azure Cosmos DB transactional store using Spark.
- Implement Change Data CaptureImplement Change Data Capture (CDC) in the Azure Cosmos DB analytical store to track data changes.
- Implement Time Travel in Microsoft FabricImplement time travel features in Warehouse within Microsoft Fabric to access historical data states.
- Azure Functions IntegrationUnderstand how to integrate Azure Functions with Azure Cosmos DB to trigger events and process data.
- Azure Event Hubs IntegrationLearn to use Azure Event Hubs for event-driven architectures with Azure Cosmos DB.
- Data Denormalization with Change FeedUtilize Change Feed and Azure Functions to denormalize data in Azure Cosmos DB.
- Referential Integrity with Change FeedImplement referential integrity in Azure Cosmos DB using Change Feed and Azure Functions.
- Data Aggregation with Change FeedPerform data aggregation for reporting purposes using Change Feed and Azure Functions.
- Data Archiving with Change FeedArchive data from Azure Cosmos DB using Change Feed and Azure Functions.
- Azure AI Search ImplementationImplement Azure AI Search to enhance search capabilities in an Azure Cosmos DB solution.
- Indexing PoliciesUnderstand how to configure and adjust indexing policies in Azure Cosmos DB to optimize query performance.
- Indexing ModesDifferentiate between consistent and lazy indexing modes and their impact on query performance.
- Query Cost CalculationCalculate the cost of a query in Azure Cosmos DB by analyzing the request units (RUs) consumed.
- Request Unit EstimationEstimate the request unit cost for point operations and queries to manage and optimize resource usage.
- Request Unit RetrievalRetrieve the request unit cost of a specific point operation or query using Azure Cosmos DB metrics.
- Integrated Cache ImplementationImplement Azure Cosmos DB integrated cache to reduce latency and improve query performance.
- Cache ConfigurationConfigure the integrated cache settings in Azure Cosmos DB to optimize data retrieval efficiency.
- Azure Functions Change Feed TriggerDevelop an Azure Functions trigger to automatically process changes from an Azure Cosmos DB change feed.
- SDK Change Feed ConsumptionConsume a change feed within an application using the Azure Cosmos DB SDK.
- Change Feed Estimator ManagementManage the number of change feed instances using the change feed estimator to optimize processing.
- Change Feed DenormalizationImplement denormalization strategies using a change feed to restructure data for optimized query performance.
- Change Feed Referential EnforcementImplement referential integrity enforcement using a change feed to maintain data consistency.
- Change Feed Aggregation PersistenceImplement aggregation persistence using a change feed to store aggregated data results.
- Change Feed Data ArchivingImplement data archiving strategies using a change feed to store historical data efficiently.
- Read-Heavy Index StrategyDetermine when to implement a read-heavy index strategy in Azure Cosmos DB to optimize read operations.
- Write-Heavy Index StrategyIdentify scenarios where a write-heavy index strategy is beneficial to optimize write operations in Azure Cosmos DB.
- Index Type SelectionChoose the appropriate index type for specific data access patterns in Azure Cosmos DB.
- Custom Indexing Policy ConfigurationConfigure a custom indexing policy using the Azure portal to tailor indexing to specific application needs.
- Composite Index ImplementationImplement composite indexes in Azure Cosmos DB to enhance query performance for multi-property queries.
- Index Performance OptimizationOptimize index performance by analyzing and adjusting indexing strategies in Azure Cosmos DB.
- Response Status Code EvaluationEvaluate response status codes to identify and troubleshoot potential issues in Azure Cosmos DB operations.
- Failure Metrics AnalysisAnalyze failure metrics to understand and address errors in Azure Cosmos DB.
- Normalized RU Consumption MonitoringMonitor the Normalized RU Consumption metric using Azure Monitor to optimize resource usage.
- Server-side Latency MonitoringMonitor server-side latency metrics using Azure Monitor to ensure efficient query performance.
- Data Replication MonitoringMonitor data replication metrics to assess latency and availability in Azure Cosmos DB.
- Azure Monitor Alerts ConfigurationConfigure Azure Monitor alerts to receive notifications about critical events in Azure Cosmos DB.
- Resource Logs ImplementationImplement and query Azure Monitor resource logs to gain insights into Azure Cosmos DB operations.
- Throughput Monitoring Across PartitionsMonitor throughput across partitions to ensure balanced and efficient data distribution.
- Data Distribution MonitoringMonitor the distribution of data across partitions to identify and resolve skew issues.
- Security Monitoring with Logging and AuditingMonitor security by using logging and auditing to detect unauthorized access and ensure compliance.
- Backup Strategy SelectionUnderstand the differences between periodic and continuous backup options in Azure Cosmos DB.
- Periodic Backup ConfigurationConfigure periodic backup settings for an Azure Cosmos DB account.
- Continuous Backup ConfigurationSet up continuous backup and understand its integration with point-in-time restore capabilities.
- Point-in-Time Restore Point IdentificationLocate and identify appropriate restore points for executing a point-in-time restore.
- Database Restore ExecutionPerform a restore operation on a database or container using a selected restore point.
- Platform-Managed Encryption KeysUnderstand how to use platform-managed encryption keys for securing data in Azure Cosmos DB.
- Customer-Managed Encryption KeysLearn how to implement customer-managed encryption keys for enhanced control over data encryption in Azure Cosmos DB.
- Network-Level Access ControlConfigure network-level access control to restrict access to Azure Cosmos DB resources.
- IP Firewall RulesSet up IP firewall rules to manage allowed IP addresses for Azure Cosmos DB access.
- Virtual Network Service EndpointsImplement virtual network service endpoints to secure Azure Cosmos DB within a virtual network.
- Private LinkUse Azure Private Link to provide secure and private connectivity to Azure Cosmos DB.
- Data Encryption at RestConfigure data encryption at rest to protect stored data in Azure Cosmos DB.
- Data Encryption in TransitEnsure data encryption in transit to secure data moving to and from Azure Cosmos DB.
- Control Plane Access ManagementManage control plane access to Azure Cosmos DB using role-based access control and Azure Active Directory.
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for DP-420, so none is invented.