What
You’ll Learn
You’ll Learn
- Create efficient data models and partition strategies tailored for scalability and performance in globally distributed applications.
- Configure access control
- encryption
- and compliance settings to protect data and meet regulatory standards.
- Monitor
- optimize
- and tune throughput
- indexing
- and consistency levels to meet performance and budgetary goals.
- Build applications using Azure Cosmos DB SDKs
- and integrate with services like Azure Functions
- Azure Logic Apps
- and Azure Event Grid.
Requirements
- Learners should have experience in cloud-based application development
- be familiar with Azure services
- and understand the basics of NoSQL databases and distributed systems.
Description
Skills at a glance
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Design and implement data models (35–40%)
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Design and implement data distribution (5–10%)
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Integrate an Azure Cosmos DB solution (5–10%)
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Optimize an Azure Cosmos DB solution (15–20%)
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Maintain an Azure Cosmos DB solution (25–30%)
Design and implement data models (35–40%)
Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
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Develop a design by storing multiple entity types in the same container
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Develop a design by storing multiple related entities in the same document
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Develop a model that denormalizes data across documents
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Develop a design by referencing between documents
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Identify primary and unique keys
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Identify data and associated access patterns
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Specify a default time to live (TTL) on a container for a transactional store
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Develop a design for versioning documents
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Develop a design for document schema versioning
Design a data partitioning strategy for Azure Cosmos DB for NoSQL
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Choose a partitioning strategy based on a specific workload
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Choose a partition key
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Plan for transactions when choosing a partition key
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Evaluate the cost of using a cross-partition query
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Calculate and evaluate data distribution based on partition key selection
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Calculate and evaluate throughput distribution based on partition key selection
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Construct and implement a synthetic partition key
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Design and implement a hierarchical partition key
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Design partitioning for workloads that require multiple partition keys
Plan and implement sizing and scaling for a database created with Azure Cosmos DB
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Evaluate the throughput and data storage requirements for a specific workload
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Choose between serverless, provisioned and free models
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Choose when to use database-level provisioned throughput
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Design for granular scale units and resource governance
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Evaluate the cost of the global distribution of data
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Configure throughput for Azure Cosmos DB by using the Azure portal
Implement client connectivity options in the Azure Cosmos DB SDK
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Choose a connectivity mode (gateway versus direct)
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Implement a connectivity mode
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Create a connection to a database
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Enable offline development by using the Azure Cosmos DB emulator
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Handle connection errors
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Implement a singleton for the client
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Specify a region for global distribution
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Configure client-side threading and parallelism options
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Enable SDK logging
Implement data access by using the SQL language for Azure Cosmos DB for NoSQL
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Implement queries that use arrays, nested objects, aggregation, and ordering
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Implement a correlated subquery
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Implement queries that use array and type-checking functions
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Implement queries that use mathematical, string, and date functions
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Implement queries based on variable data
Implement data access by using Azure Cosmos DB for NoSQL SDKs
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Choose when to use a point operation versus a query operation
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Implement a point operation that creates, updates, and deletes documents
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Implement an update by using a patch operation
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Manage multi-document transactions using SDK Transactional Batch
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Perform a multi-document load using Bulk Support in the SDK
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Implement optimistic concurrency control using ETags
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Override default consistency by using query request options
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Implement session consistency by using session tokens
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Implement a query operation that includes pagination
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Implement a query operation by using a continuation token
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Handle transient errors and 429s
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Specify TTL for a document
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Retrieve and use query metrics
Implement server-side programming in Azure Cosmos DB for NoSQL by using JavaScript
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Write, deploy, and call a stored procedure
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Design stored procedures to work with multiple documents transactionally
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Implement and call triggers
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Implement a user-defined function
Design and implement data distribution (5–10%)
Design and implement a replication strategy for Azure Cosmos DB
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Choose when to distribute data
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Define automatic failover policies for regional failure for Azure Cosmos DB for NoSQL
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Perform manual failovers to move single master write regions
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Choose a consistency model
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Identify use cases for different consistency models
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Evaluate the impact of consistency model choices on availability and associated request unit (RU) cost
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Evaluate the impact of consistency model choices on performance and latency
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Specify application connections to replicated data
Design and implement multi-region write
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Choose when to use multi-region write
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Implement multi-region write
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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
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Enable Azure Synapse Link
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Choose between Azure Synapse Link and Spark Connector
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Enable the analytical store on a container
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Implement custom partitioning in Azure Synapse Link
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Enable a connection to an analytical store and query from Azure Synapse Spark or Azure Synapse SQL
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Perform a query against the transactional store from Spark
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Write data back to the transactional store from Spark
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Implement Change Data Capture in the Azure Cosmos DB analytical store
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Implement time travel in Azure Synapse Link for Azure Cosmos DB
Implement solutions across services
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Integrate events with other applications by using Azure Functions and Azure Event Hubs
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Denormalize data by using Change Feed and Azure Functions
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Enforce referential integrity by using Change Feed and Azure Functions
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Aggregate data by using Change Feed and Azure Functions, including reporting
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Archive data by using Change Feed and Azure Functions
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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
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Adjust indexes on the database
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Calculate the cost of the query
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Retrieve request unit cost of a point operation or query
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Implement Azure Cosmos DB integrated cache
Design and implement change feeds for Azure Cosmos DB for NoSQL
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Develop an Azure Functions trigger to process a change feed
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Consume a change feed from within an application by using the SDK
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Manage the number of change feed instances by using the change feed estimator
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Implement denormalization by using a change feed
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Implement referential enforcement by using a change feed
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Implement aggregation persistence by using a change feed
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Implement data archiving by using a change feed
Define and implement an indexing strategy for Azure Cosmos DB for NoSQL
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Choose when to use a read-heavy versus write-heavy index strategy
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Choose an appropriate index type
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Configure a custom indexing policy by using the Azure portal
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Implement a composite index
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Optimize index performance
Maintain an Azure Cosmos DB solution (25–30%)
Monitor and troubleshoot an Azure Cosmos DB solution
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Evaluate response status code and failure metrics
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Monitor metrics for normalized throughput usage by using Azure Monitor
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Monitor server-side latency metrics by using Azure Monitor
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Monitor data replication in relation to latency and availability
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Configure Azure Monitor alerts for Azure Cosmos DB
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Implement and query Azure Cosmos DB logs
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Monitor throughput across partitions
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Monitor distribution of data across partitions
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Monitor security by using logging and auditing
Implement backup and restore for an Azure Cosmos DB solution
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Choose between periodic and continuous backup
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Configure periodic backup
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Configure continuous backup and recovery
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Locate a recovery point for a point-in-time recovery
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Recover a database or container from a recovery point
Implement security for an Azure Cosmos DB solution
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Choose between service-managed and customer-managed encryption keys
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Configure network-level access control for Azure Cosmos DB
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Configure data encryption for Azure Cosmos DB
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Manage control plane access to Azure Cosmos DB by using Azure role-based access control (RBAC)
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Manage control plane access to Azure Cosmos DB Data Explorer by using Azure role-based access control (RBAC)
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Manage data plane access to Azure Cosmos DB by using Microsoft Entra ID
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Configure cross-origin resource sharing (CORS) settings
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Manage account keys by using Azure Key Vault
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Implement customer-managed keys for encryption
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Implement Always Encrypted
Implement data movement for an Azure Cosmos DB solution
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Choose a data movement strategy
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Move data by using client SDK bulk operations
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Move data by using Azure Data Factory and Azure Synapse pipelines
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Move data by using a Kafka connector
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Move data by using Azure Stream Analytics
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Move data by using the Azure Cosmos DB Spark Connector
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Configure Azure Cosmos DB as a custom endpoint for an Azure IoT Hub
Implement a DevOps process for an Azure Cosmos DB solution
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Choose when to use declarative versus imperative operations
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Provision and manage Azure Cosmos DB resources by using Azure Resource Manager templates
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Migrate between standard and autoscale throughput by using PowerShell or Azure CLI
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Initiate a regional failover by using PowerShell or Azure CLI
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Maintain indexing policies in production by using Azure Resource Manager templates
Who this course is for:
- This certification is ideal for developers and solution architects who design and build cloud-native applications that use Azure Cosmos DB as their primary data store.
