Update section 01 and 02

Author: codingbandit

01_Azure_Overview/README.md

@@ -99,7 +99,7 @@ Launch the Cloud Shell in a browser at [https://shell.azure.com](https://shell.a
 
 #### PowerShell Module
 
-The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for Mongo DB API. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/powershell/module/az.cosmosdb/) for Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
+The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for NoSQL. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/powershell/module/az.cosmosdb/) for Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
 
 ```PowerShell
 Install-Module -Name Az.CosmosDB

02_Overview_Cosmos_DB/README.md

@@ -4,24 +4,20 @@
 
 ## Azure Cosmos DB and AI
 
-The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance. Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data. Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models, making it an ideal platform for AI applications. The wide array of data model support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
+The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance. Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data. In particular, its support for vector storage and retrieval is a game-changer for generative AI applications. By representing complex data elements like text, images, or sound as high-dimensional vectors, Cosmos DB allows for efficient storage, indexing, and querying of these vectors, which is crucial for many generative AI tasks.
+
+Unlike traditional databases requiring separate workarounds for different data types, Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models within a single, integrated environment. This simplification means you can leverage the same robust platform for all your AI data needs. Many AI applications rely on external stand-alone vector stores, which can be cumbersome to manage and maintain. Azure Cosmos DB's native support for vector storage and retrieval eliminates the need for these external stores as all the application's data is located in a single place thus streamlining the development and deployment of AI applications. These features enable the building, deploying, and scaling of AI applications to be more efficient and reliable, making Azure Cosmos DB an ideal choice for handling the complex data requirements of modern generative AI solutions.
 
 ## Azure Cosmos DB for NoSQL
 
 The focus for this developer guide is [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/) and [Vector Search](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).
 
-### Azure Cosmos DB for NoSQL Architectures
-
-The [RU architecture](https://learn.microsoft.com/azure/cosmos-db/request-units) for Azure Cosmos DB for NoSQL offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, unlimited scalability, real-time analytics, and serverless deployments paying only per operation.
-
-[Azure Cosmos DB for NoSQL architecture](https://learn.microsoft.com/azure/cosmos-db/convert-vcore-to-request-unit) integrates AI-based applications with private organizational data, with text indexing for easy querying. Simplify the development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
-
-The [choice between vCore and Request Units (RU)](hhttps://learn.microsoft.com/azure/cosmos-db/convert-vcore-to-request-unit) in Azure Cosmos DB for NoSQL API depends on the workload. A list of [compatibility and feature support between RU and vCore](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/compatibility) is available.
+### Azure Cosmos DB for NoSQL capacity modes
 
-vCore provides predictable performance and cost and is ideal for running high-performance, mission-critical workloads with low latency and high throughput. With vCore, the number of vCPUs and the memory the database needs is configurable and can be scaled up or down as needed.
+Azure Cosmos DB offers three capacity modes: provisioned throughput, serverless and autoscale modes. creating an Azure Cosmos DB account, it's essential to evaluate the workload's characteristics in order to choose the appropriate mode to optimize both performance and cost efficiency.
 
-Conversely, RU is a consumption-based model that charges based on the number of operations the database performs, including reads, writes, and queries. RU is ideal for scenarios where the workload has unpredictable traffic patterns or a need to optimize cost for bursty workloads.
+[**Provisioned throughput mode**](https://learn.microsoft.com/azure/cosmos-db/set-throughput) allocates a fixed amount of resources, measured in [Request Units per second (RUs/s)](https://learn.microsoft.com/azure/cosmos-db/request-units), which is ideal for applications with predictable and steady workloads. This ensures consistent performance and can be more cost-effective when there is a constant or high demand for database operations. RU/s can be set at both the database and container levels, allowing for fine-grained control over resource allocation.
 
-A steady-state workload with predictable traffic patterns is best suited for vCore since it provides more predictable performance and cost. However, RU may be a better choice if the workload has unpredictable traffic patterns or requires bursty performance since it allows for paying only for the resources used.
+[**Serverless mode**](https://learn.microsoft.com/en-us/azure/cosmos-db/serverless) offers a more flexible and pay-as-you-go approach, where only the Request Units consumed are billed. This is particularly advantageous for applications with sporadic or unpredictable usage patterns, as it eliminates the need to provision resources upfront.
 
->**NOTE**: AI-supporting workloads, such as vector search, must use the vCore architecture, as vector search is not supported with RU accounts.
+[**Autoscale mode**](https://learn.microsoft.com/azure/cosmos-db/provision-throughput-autoscale) builds upon the provisioned throughput mode but allows for the database or container automatically and instantly scales up or down resources based on demand, ensuring that the application can handle varying workloads efficiently. When configuring autoscale, a maximum (Tmax) value threshold is set for a predictable maximum cost. This mode is suitable for applications with fluctuating usage patterns or infrequently used applications.