This page lists exercises associated with Microsoft skilling content on Microsoft Learn
Lab 1 - Create and connect to a cluster
Level: 300 | Duration: 20
In this exercise, you create an Azure DocumentDB cluster, connect to it using MongoDB Shell, and run test queries.
Lab 2 - Query and manipulate e-commerce data
Level: 300 | Duration: 25
Practice inserting, querying, updating, and aggregating documents in an Azure DocumentDB cluster using an e-commerce scenario.
Lab 3 - Build a product management application - C#
Level: 300 | Duration: 30
Build a console application that connects to Azure DocumentDB and performs CRUD operations using the MongoDB driver for .NET.
Lab 3 - Build a product management application - Node.js
Level: 300 | Duration: 30
Build a console application that connects to Azure DocumentDB and performs CRUD operations using the MongoDB driver for Node.js.
Lab 3 - Build a product management application - Python
Level: 300 | Duration: 30
Build a console application that connects to Azure DocumentDB and performs CRUD operations using the MongoDB driver for Python.
Lab 4 - Design a relationship model for an e-commerce platform
Level: 300 | Duration: 30
Apply relationship modeling patterns to design and implement a data model for an e-commerce platform in Azure DocumentDB, using embedding, referencing, subset, and many-to-many patterns.
Lab 5 - Apply patterns to the e-commerce platform
Level: 300 | Duration: 30
Apply schema design patterns to an e-commerce platform in Azure DocumentDB. Implement the inheritance, computed, subset, and single collection patterns with hands-on exercises.
Lab 6 - Identify and fix anti-patterns
Level: 300 | Duration: 30
Identify and fix schema design anti-patterns in an Azure DocumentDB e-commerce database, including unbounded arrays, overlapping indexes, over-normalization, and case-sensitivity issues.
Lab 7 - Build an Indexing Strategy for the E-Commerce Platform
Level: 300 | Duration: 30
Build an indexing strategy for an e-commerce platform in Azure DocumentDB. Create compound indexes using the ESR rule and verify performance improvements with the explain() command.