DP-420T00: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB

• Design and implement data models (35–40%)

• Design and implement data distribution (5–10%)

• Integrate an Azure Cosmos DB solution (5–10%)

• Optimize an Azure Cosmos DB solution (15–20%)

• Maintain an Azure Cosmos DB solution (25–30%)

• 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

• 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

• 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

• 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 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

• 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

• 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

• 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 RU cost

• Evaluate the impact of consistency model choices on performance and latency

• Specify application connections to replicated data

• Choose when to use multi-region write

• Implement multi-region write

• Implement a custom conflict resolution policy for Azure Cosmos DB for NoSQL

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 Azure Active Directory (Azure AD)

• Configure Cross-Origin Resource Sharing (CORS) settings

• Manage account keys by using Azure Key Vault

• Implement customer-managed keys for encryption

• Implement Always Encrypted

• 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

• 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 indexing policies in production by using ARM templates

Before attending this course, students must have:

• Knowledge of Microsoft Azure and ability to navigate the Azure portal (AZ-900 equivalent)

• Experience writing in an Azure-supported language at the intermediate level. (C#, JavaScript, Python, or Java)

• Ability to write code to connect and perform operations on a SQL or NoSQL database product. (SQL Server, Oracle, MongoDB, Cassandra or similar)