Azure Exam DP-420: Study Guide Designing and Implementing CloudNative Applications Using Microsoft Azure Cosmos DB (Beta)

The following will be my study guide for the exam along with the following labs:-

https://azurecosmosdb.github.io/labs/

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Design and Implement Data Models (35–40%)

Design and implement a non-relational data model for Azure Cosmos DB Core API

• 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 TTL on a container for a transactional store
  
  Design a data partitioning strategy for Azure Cosmos DB Core API
  
• choose a partition 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 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 and provisioned 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 Azure Cosmos DB SQL language
• 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 SQL API 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 SDK Bulk
• implement optimistic concurrency control using ETags
• 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 documenthttps://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels
• retrieve and use query metrics
  
  Implement server-side programming in Azure Cosmos DB Core API by using JavaScript
• write, deploy, and call a stored procedure
• design stored procedures to work with multiple items transactionally
• implement triggers
• implement a user-defined function
  
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  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 Core API
• 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 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 Core API
  
  —
  
  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
• 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 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 Cognitive Search for an Azure Cosmos DB solution
  
  —
  
  Optimize an Azure Cosmos DB Solution (15–20%)
  
  Optimize query performance in Azure Cosmos DB Core API
• 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 an Azure Cosmos DB Core API
• 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 an Azure Cosmos DB Core API
• 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 Azure Active Directory
• 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
  
  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 (ARM templates)
• migrate between standard and autoscale throughput by using PowerShell or Azure CLI
• initiate a regional failover by using PowerShell or Azure CLI
• maintain index policies in production by using ARM templates

Remember a lot of this content is covered in the following GitHub labs:-

https://azurecosmosdb.github.io/labs/

Enjoy!

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