The following will be my study guide for the exam along with the following labs:-
https://azurecosmosdb.github.io/labs/
Don’t forget to subscribe to my YouTube Channel.
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
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!
Don’t forget to subscribe to my YouTube Channel.
