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Exam DP-420: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (Video)

By Tim Warner
Published Jan 5, 2023 by Microsoft Press.

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Description

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Description

Copyright 2023
Edition: 1st

Online Video
ISBN-10: 0-13-795122-1
ISBN-13: 978-0-13-795122-2

11+ Hours of Video Instruction

Microsoft MVP and Microsoft Certified Azure Solutions Architect Tim Warner walks you through what to expect on the DP-420 exam. This course provides everything you need to know to pass this exam.

Cosmos DB is Microsoft Azure's flagship non-relational database product. Cosmos DB supports multiple application programming interfaces (APIs) that provide compatibility with competing NoSQL databases. Cosmos DB also features five different transaction consistency levels, as well as multimaster global replication. Businesses that need to integrate enterprise-scale, cloud-based, NoSQL databases in their solutions will obviously consider Cosmos DB. Learners can embrace this training for real-world Cosmos DB training.

There's a decided emphasis on building hands-on skills, because this is how Microsoft focuses their Azure Cosmos DB Database Developer job role.

Skill Level

Intermediate

What You Will Learn

Design and implement data models
Design and implement distributed data
Integrate, optimize, and maintain an Azure Cosmos DB solution

Who Should Take This Course

Database Administrator
Database Developer
Data Architect
Data Scientist
Data Analyst
Infrastructure Architect
Software Developer
DevOps Engineer

Course Requirements

Prerequisites:

A candidate for the Azure Cosmos DB Developer Specialty certification should have subject matter expertise designing, implementing, and monitoring cloud-native applications that store and manage data. Intermediate understanding of both relational and non-relational databases and Intermediate experience with Microsoft Azure.

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Sample Content

Introduction

Module 1: Design and Implement Data Models

Lesson 1: Design and Implement a Non-Relational Data Model for Azure Cosmos DB Core API

1.1 Develop a design by storing multiple entity types in the same container

1.2 Develop a design by storing multiple related entities in the same document

1.3 Develop a design by referencing between documents

1.4 Identify primary and unique keys

1.5 Identify data and associated access patterns

1.6 Specify a default TTL on a container for a transactional store

Lesson 2: Design a Data-Partitioning Strategy for Azure Cosmos DB Core API

2.1 Choose a partition strategy based on a specific workload

2.2 Plan for transactions when choosing a partition key

2.3 Calculate and evaluate throughput distribution based on partition key selection

2.4 Design partitioning for workloads that require multiple partition keys

Lesson 3: Plan and Implement Sizing and Scaling for a Database Created with Azure Cosmos DB

3.1 Choose between serverless and provisioned models

3.2 Choose when to use database-level provisioned throughput

3.3 Design for granular scale units and resource governance

3.4 Evaluate the cost of the global distribution of data

3.5 Configure throughput for Azure Cosmos DB by using the Azure portal

Lesson 4: Implement Client Connectivity Options in the Azure Cosmos DB SDK

4.1 Implement a connectivity mode

4.2 Create a connection to a database

4.3 Enable offline development by using the Azure Cosmos DB emulator

4.4 Handle connection errors

4.5 Configure client-side threading and parallelism options

4.6 Enable SDK logging

Lesson 5: Implement Data Access by Using the Azure Cosmos DB SQL Language

5.1 Implement queries that use arrays, nested objects, aggregation, and ordering

5.2 Implement a correlated subquery

5.3 Implement queries that use array and type-checking functions

5.4 Implement queries that use mathematical, string, and date functions

5.5 Implement queries based on variable data

Lesson 6: Implement Data Access by Using SQL API SDKs

6.1 Choose when to use a point operation versus a query operation

6.2 Implement a point operation that creates, updates, and deletes documents

6.3 Implement an update by using a patch operation

6.4 Manage multi-document transactions using SDK Transactional Batch

6.5 Perform a multi-document load using SDK Bulk

6.6 Implement optimistic concurrency control using ETags

6.7 Implement session consistency by using session tokens

6.8 Implement a query operation that includes pagination

6.9 Implement a query operation by using a continuation token

6.10 Handle transient errors and 429s

6.11 Specify TTL for a document

6.12 Retrieve and use query metrics

Lesson 7: Implement Server-Side Programming in Azure Cosmos DB Core API by Using JavaScript

7.1 Design stored procedures to work with multiple items transactionally

7.2 Implement triggers

7.3 Implement a user-defined function

Module 2: Design and Implement Data Distribution

Lesson 8: Design and Implement a Replication Strategy for Azure Cosmos DB

8.1 Choose when to distribute data

8.2 Define automatic failover policies for regional failure for Azure Cosmos DB Core API

8.3 Perform manual failovers to move single master write regions

8.4 Specify application connections to replicated data

Lesson 9: Design and Implement Multi-Region Writes

9.1 Choose when to use multi-region writes

9.2 Implement multi-region write

9.3 Implement a custom conflict resolution policy for Azure Cosmos DB Core API

Module 3: Integrate an Azure Cosmos DB Solution

Lesson 10: Enable Azure Cosmos DB Analytical Workloads

10.1 Enable Azure Synapse Link

10.2 Choose between Azure Synapse Link and Spark Connector

10.3 Enable the analytical store on a container

10.4 Write data back to the transactional store from Spark

Lesson 11: Implement Solutions Across Services

11.1 Integrate events with other applications by using Azure Functions and Azure Event Hubs

11.2 Denormalize data by using Change Feed and Azure Functions

11.3 Archive data by using Change Feed and Azure Functions

11.4 Implement Azure Cognitive Search for an Azure Cosmos DB solution

Module 4: Optimize an Azure Cosmos DB Solution

Lesson 12: Optimize Query Performance in Azure Cosmos DB Core API

12.1 Adjust indexes on the database

12.2 Calculate the cost of the query

12.3 Retrieve request unit cost of a point operation or query

12.4 Implement Azure Cosmos DB integrated cache

Lesson 13: Design and Implement Change Feeds for an Azure Cosmos DB Core API

13.1 Implement data archiving by using a change feed

Lesson 14: Define and Implement an Indexing Strategy for an Azure Cosmos DB Core API

14.1 Choose when to use a read-heavy versus write-heavy index strategy

14.2 Choose an appropriate index type

14.3 Configure a custom indexing policy by using the Azure portal

14.4 Implement a composite index

14.5 Optimize index performance

Module 5: Maintain an Azure Cosmos DB Solution

Lesson 15: Monitor and Troubleshoot an Azure Cosmos DB Solution

15.1 Evaluate response status code and failure metrics

15.2 Monitor data replication in relation to latency and availability

15.3 Configure Azure Monitor alerts for Azure Cosmos DB

15.4 Implement and query Azure Cosmos DB logs

15.5 Monitor distribution of data across partitions

15.6 Monitor security by using logging and auditing

Lesson 16: Implement Backup and Restore for an Azure Cosmos DB Solution

16.1 Choose between periodic and continuous backup

16.2 Configure periodic backup

16.3 Configure continuous backup and recovery

16.4 Recover a database or container from a recovery point

Lesson 17: Implement Security for an Azure Cosmos DB Solution

17.1 Choose between service-managed and customer-managed encryption keys

17.2 Configure network-level access control for Azure Cosmos DB

17.3 Configure data encryption for Azure Cosmos DB

17.4 Manage control plane access to Azure Cosmos DB by using Azure role-based access control (RBAC)

17.5 Manage data plane access to Azure Cosmos DB by using Azure Active Directory

17.6 Configure Cross-Origin Resource Sharing (CORS) settings

17.7 Manage account keys by using Azure Key Vault

17.8 Implement customer-managed keys for encryption

17.9 Implement Always Encrypted

Lesson 18: Implement Data Movement for an Azure Cosmos DB Solution

18.1 Choose a data movement strategy

18.2 Move data by using client SDK bulk operations

18.3 Move data by using Azure Data Factory and Azure Synapse pipelines

18.4 Move data by using a Kafka connector

18.5 Move data by using Azure Stream Analytics

18.6 Move data by using the Azure Cosmos DB Spark Connector

Lesson 19: Implement a DevOps Process for an Azure Cosmos DB Solution

19.1 Choose when to use declarative versus imperative operations

19.2 Provision and manage Azure Cosmos DB resources by using Azure Resource Manager templates (ARM templates)

19.3 Migrate between standard and autoscale throughput by using PowerShell or Azure CLI

19.4 Initiate a regional failover by using PowerShell or Azure CLI

19.5 Maintain index policies in production by using ARM templates

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