Correct Options:

1. A Workspace (F):
   • Explanation: An Azure Machine Learning workspace is a central place where you manage all your machine learning resources. It allows you to isolate experiments, datasets, and other resources, ensuring that each group of data scientists can access only their own experiments. Workspaces also support role-based access control (RBAC), enabling team members to grant permissions to others within their workspace¹.
2. Owner (E):
   • Explanation: The Owner role in Azure provides full access to all resources, including the ability to delegate access to others. This role is necessary for team members who need to manage permissions and grant access to other members of their team².

Incorrect Options:

1. A resource group (A):
   • Explanation: While a resource group can contain multiple Azure resources, it does not provide the granularity needed to isolate experiments for different groups of data scientists. Resource groups are more suited for organizing resources rather than managing detailed access controls¹.
2. Reader (B):
   • Explanation: The Reader role provides read-only access to resources. It does not allow users to create or manage experiments, nor does it enable them to grant permissions to others. This role is insufficient for the requirements stated in the question².
3. Contributor ©:
   • Explanation: The Contributor role allows users to create and manage resources but does not include the ability to grant permissions to others. While it provides more access than the Reader role, it still falls short of the requirements for managing team permissions².
4. A subscription (D):
   • Explanation: A subscription is a billing entity that can contain multiple resource groups and resources. It is too broad for the specific requirement of isolating experiments and managing permissions at the team level. Subscriptions are not designed for fine-grained access control¹.

By selecting A Workspace (F) and Owner (E), you can ensure that each group of data scientists has isolated access to their experiments and the ability to manage permissions within their team.

References:

Instead load key to node to access using SSH connection.

All requests to a search service need a read-only api-key that was generated specifically for your service. The api-key is the sole mechanism for authenticating access to your search service endpoint and must be included on every request.
References:
https://docs.microsoft.com/en-us/azure/search/search-security-api-keys

All Options and correct answers:

References:

By using Azure services such as the Computer Vision API and Azure Functions, companies can eliminate the need to manage individual servers, while reducing costs and leveraging the expertise that Microsoft has already developed around processing images with Cognitive Services. This example scenario specifically addresses an image-processing use case. If you have different AI needs, consider the full suite of Cognitive Services.
use cases include:
> Classifying images on a fashion website.
> Classifying telemetry data from screenshots of games.

This scenario covers the back-end components of a web or mobile application. Data flows through the scenario as follows:
The API layer is built using Azure Functions. These APIs enable the application to upload images and retrieve data from Cosmos DB. When an image is uploaded via an API call, it’s stored in Blob storage.
Adding new files to Blob storage triggers an Event Grid notification to be sent to an Azure Function.
Azure Functions sends a link to the newly uploaded file to the Computer Vision API to analyze.
Once the data has been returned from the Computer Vision API, Azure Functions makes an entry in Cosmos DB to persist the results of the analysis along with the image metadata.
References:
https://docs.microsoft.com/en-us/azure/architecture/example-scenario/ai/intelligent-apps-image-processing

The most suitable service to control the type, size, and location of resources provisioned by your AI developers in Azure is Azure Policy.

Here’s why Azure Policy is the best fit for this scenario:

• Resource Management: Azure Policy allows you to define policies that enforce specific rules on resources deployed in Azure. These policies can restrict the type (e.g., virtual machine types), size (e.SKU), and location (e.g., specific regions) of resources that developers can provision.  
• Centralized Control: With Azure Policy, you can define policies centrally and apply them to subscriptions, resource groups, or specific resources. This ensures consistent enforcement across your entire Azure environment.
• Granular Control: You can create different policies for different developer groups or projects, allowing for granular control over resource provisioning.  
• Other options and why they are not the best fit:

• Azure Key Vault: While useful for storing secrets securely, it doesn’t directly control resource provisioning.
• Azure Security Center: Primarily focuses on security posture and threat detection, not resource provisioning control.
• Azure Managed Identities: Provide identities for Azure resources without human intervention, but don’t enforce resource provisioning policies.
• Azure Service Principals: Similar to managed identities, they provide authentication for Azure resources but lack policy enforcement capabilities.

By implementing Azure Policy, you can ensure that your AI developers have the resources they need while maintaining control over costs and security.

The correct answer is A. Downscale the cluster to zero nodes.

Explanation:

• Downscaling to zero nodes: This option allows you to completely stop the cluster, eliminating all compute costs. When you need to run the Azure Batch AI process, you can simply scale the cluster back up to the desired number of nodes. This provides a cost-effective solution for infrequent, scheduled workloads.

Incorrect Options:

• Downscaling to one node: While this option can reduce costs compared to running five nodes, it still incurs some compute costs. If the workload is very infrequent, it might be more cost-effective to completely stop the cluster.
• Deleting the cluster: This option is not ideal because it requires recreating the cluster each time you need to run the Azure Batch AI process. This can be time-consuming and may incur additional costs, especially if you have complex cluster configurations.

Azure Event Hubs is a big data streaming platform and event ingestion service. It can receive and process millions of events per second. Data sent to an event hub can be transformed and stored by using any real-time analytics provider or batching/storage adapters.
The following scenarios are some of the scenarios where you can use Event Hubs:
Anomaly detection (fraud/outliers)
Application logging
Analytics pipelines, such as click-streams
Live dash-boarding
Archiving data
Transaction processing
User telemetry processing
Device telemetry streaming
References:
https://docs.microsoft.com/en-in/azure/event-hubs/event-hubs-about

All Options with correct Answer:
Azure data Lake: A common big data scenario is batch processing of data at rest. In this scenario, the source data is loaded into data storage, either by the source application itself or by an orchestration workflow. The data is then processed in-place by a parallelized job, which can also be initiated by the orchestration workflow. The processing may include multiple iterative steps before the transformed results are loaded into an analytical data store, which can be queried by analytics and reporting components.
If you have unstructured text or images in Azure Blob storage, an AI enrichment pipeline can extract information and create new content that is useful for full-text search or knowledge mining scenarios. Although a pipeline can process images, this REST tutorial focuses on text, applying language detection and natural language processing to create new fields that you can leverage in queries, facets, and filters.
This tutorial uses Postman and the Search REST APIs to perform the following tasks:
Start with whole documents (unstructured text) such as PDF, HTML, DOCX, and PPTX in Azure Blob storage.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/batch-processing
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://www.sqlchick.com/entries/2017/9/4/querying-multi-structured-json-files-with-u-sql-in-azure-data-lake
https://docs.microsoft.com/en-us/azure/search/cognitive-search-tutorial-blob

Instead load key to node to access using SSH connection.

All Options with correct answers:
The following diagram shows how Azure Storage uses Azure Active Directory and Azure Key Vault to make requests using the customer-managed key
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-encryption-keys-portal
https://docs.microsoft.com/en-us/azure/key-vault/keys/hsm-protected-keys
https://docs.microsoft.com/en-us/azure/storage/common/encryption-customer-managed-keys

Azure Blob Storage on IoT Edge provides a block blob and append blob storage solution at the edge. A blob storage module on your IoT Edge device behaves like an Azure blob service, except the blobs are stored locally on your IoT Edge device. You can access your blobs using the same Azure storage SDK methods or blob API calls that you’re already used to. This article explains the concepts related to Azure Blob Storage on IoT Edge container that runs a blob service on your IoT Edge device.
References:
https://docs.microsoft.com/en-us/azure/iot-edge/how-to-store-data-blob
https://youtu.be/xbwgMNGB_3Y

Export Data :
The Export data to Hive option in the Export Data module in Azure Machine Learning Studio. This option is useful when you are working with very large datasets, and want to save your machine learning experiment data to a Hadoop cluster or HDInsight distributed storage.

Apache Hive:
Apache Hive is a data warehouse system for Apache Hadoop. Hive enables data summarization, querying, and analysis of data. Hive queries are written in HiveQL, which is a query language similar to SQL.

Azure Data Lake:
Default storage for the HDFS file system of HDInsight clusters can be associated with either an Azure Storage account or an Azure Data Lake Storage.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-to-hive-query
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/hdinsight-use-hive

Azure Table storage can be used as data store for structure NoSQL data with lower cost than Azure Cosmos DB.A NoSQL key-value store for rapid development using massive semi-structured datasets
Use Azure Table storage to store petabytes of semi-structured data and keep costs down.
References
https://azure.microsoft.com/en-us/services/storage/tables/

Options with correct order:
Step 1: Create a storage container ASA Edge jobs run in containers deployed to Azure IoT Edge devices.
Step 2: Create an Azure Stream Analytics Edge Job Azure Stream Analytics (ASA) on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device-generated data.
Step 3: Add the job to the IoT devices in IoT

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

Which features of your web or mobile app are most popular? Do your users achieve their goals with your app? Do they drop out at particular points, and do they return later? Azure Application Insights helps you gain powerful insights into how people use your app. Every time you update your app, you can assess how well it works for users. With this knowledge, you can make data driven decisions about your next development cycles.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/app/usage-overview

A. SQL

• Correctness: This option is correct. SQL Server is indeed a valid compute context for running rx functions. It allows for executing machine learning tasks directly within a SQL Server environment, leveraging its computational capabilities.

B. Local sequential

• Correctness: This option remains incorrect. This context runs computations sequentially on a single machine, which is not suitable for parallel processing..

C. HBase

• Correctness: This option is incorrect. HBase is not listed as a compute context for running rx functions in Microsoft Machine Learning Server. It serves as a data storage solution rather than a compute environment.

D. Local parallel

• Correctness: This option is correct. The Local Parallel compute context enables parallel execution of rx functions on a single machine using multiple cores, making it suitable for improving performance with larger datasets.

E. Spark

• Correctness: This option is correct. The Spark compute context allows for distributed computing using Apache Spark, which is ideal for big data workloads and enables parallel execution of rx functions across multiple nodes.

A data factory can have one or more pipelines. A pipeline is a logical grouping of activities that together perform a task. For example, a pipeline could contain a set of activities that ingest and clean log data, and then kick off a mapping data flow to analyze the log data. The pipeline allows you to manage the activities as a set instead of each one individually. You deploy and schedule the pipeline instead of the activities independently.
Data Factory has three groupings of activities: data movement activities, data transformation activities, and control activities. An activity can take zero or more input datasets and produce one or more output datasets. The following diagram shows the relationship between pipeline, activity, and dataset in Data Factory:
References:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipelines-activities

A composite entity is made up of other entities that form parts of a whole.
Preferences:
https://azure.github.io/LearnAI-DesigningandArchitectingIntelligentAgents/03-luis/1_session.html

QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.
References:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

After creating a Cognitive Service resource in the Azure portal you’ll get an endpoint and a key for authenticating your applications. Create a new Azure Cognitive Services resource:
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account

Text Analytics – The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
Text Analytics uses Language APIs for sentiment analysis
References:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/

LUIS language understanding
Build applications capable of understanding natural language. Using machine teaching technology and our visual user interface, developers and subject matter experts can build custom machine-learned language models that interprets user goals and extracts key information from conversational phrases—all without any machine learning experience.
https://azure.microsoft.com/en-us/services/cognitive-services/language-understanding-intelligent-service/

QnA Maker: 5
If the user plans to support multiple languages, they need to have a new QnA Maker resource for each language.
LUIS: 5 If you need a multi-language LUIS client application such as a chatbot, you have a few options. If LUIS
supports all the languages, you develop a LUIS app for each language. Each LUIS app has a unique app ID,
and endpoint log. If you need to provide language understanding for a language LUIS does not support, you
can use Microsoft Translator API to translate the utterance into a supported language, submit the utterance to
the LUIS endpoint, and receive the resulting scores.
Language detection: 1 The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown. You can
parse the results of this analysis to determine which language is used in the input document. The response
also returns a score that reflects the confidence of the model. The score value is between 0 and 1.
The Language Detection feature can detect a wide range of languages, variants, dialects, and some regional
or cultural languages. The exact list of languages for this feature isn’t published.

Azure Storage blob is a managed storage service that is highly available, secure, durable, scalable, and redundant. Microsoft takes care of maintenance and handles critical problems for you. Azure Storage is the most ubiquitous storage solution Azure provides, due to the number of services and tools that can be used with it.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage

References:

Computer Vision  can detect text in an image using optical character recognition (OCR) and extract the recognized words into a machine-readable character stream
References:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision

Box 1: Azure Container Registry – Azure Container Registry enables storage of images for all types of Docker container deployments including DC/OS Docker Swarm and Kubernetes. The scoring images are deployed as containers on Azure Kubernetes Service and used to run the scoring script. The image used here is created by Machine Learning from the trained model and scoring script and then is pushed to the Azure Container Registry.
Box 2: Azure Kubernetes Services – Azure Kubernetes Service (AKS) is used to deploy the application on a Kubernetes cluster. AKS simplifies the deployment and operations of Kubernetes. The cluster can be configured using CPU-only VMs for regular Python models or GPU-enabled VMs for deep learning models. This reference architecture shows how to deploy Python models as web services to make real-time predictions using the Azure Machine Learning.

References:
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/machine-learning-with-aks

People and place are Name entity recognition also need translator text as using several languages
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, language detection, and named entity recognition.
Named Entity Recognition
Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

Options with correct Answer:
Azure Bot Service –
help you enrich the customer experience while maintaining control of your data. Build any type of bot from a Q&A bot to your own branded virtual assistant—to quickly connect your users to the answers they need.
Computer Vision –
The Computer Vision Analyze an image feature, returns information about visual content found in an image. Use tagging, domain-specific models, and descriptions in four languages to identify content and label it with confidence. Use Object Detection to get location of thousands of objects within an image. Apply the adult/racy settings to help you detect potential adult content. Identify image types and color schemes in pictures.
References:
https://azure.microsoft.com/en-us/services/bot-service/
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/

A container is a standard unit of software that packages up code and all its dependencies so the
application runs quickly and reliably from one computing environment to another. A Docker
container image is a lightweight, standalone, executable package of software that includes everything
needed to run an application: code, runtime, system tools, system libraries and settings.
deploy the Cognitive Services Face container to Azure Container Instances. This procedure demonstrates the creation of an Azure Face resource. Then we discuss pulling the associated container image. Finally, we highlight the ability to exercise the orchestration of the two from a browser. Using containers can shift the developers’ attention away from managing infrastructure to instead focusing on application development.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/deploy-face-container-on-container-instances

Content Moderator – The Azure Content Moderator API is a cognitive service that checks text image and video content for material that is potentially offensive risky or otherwise undesirable. When such material is found the service applies appropriate labels (flags) to the content. Your app can then handle flagged content in order to comply with regulations or maintain the intended environment for users.
Language Understanding (LUIS) – Designed to identify valuable information in conversations LUIS interprets user goals (intents) and distills valuable information from sentences (entities) for a high quality nuanced language model. LUIS integrates seamlessly with the Azure Bot Service making it easy to create a sophisticated bot.
Text Analytics – The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text and includes four main functions: sentiment analysis key phrase extraction named entity recognition and language detection.

You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. For example you can deploy an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data
Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data
References:
https://docs.microsoft.com/bs-latn-ba/azure/iot-edge/tutorial-deploy-machine-learning

All Options with Correct Answers:

References:

Prebuilt:  date time value: 8 dec
Language Understanding (LUIS) provides prebuilt entities. When a prebuilt entity is included in your application, LUIS includes the corresponding entity prediction in the endpoint response. All example utterances are also labeled with the entity. The behavior of prebuilt entities can’t be modified. Unless otherwise noted, prebuilt entities are available in all LUIS application locales (cultures). The following table shows the prebuilt entities that are supported for each culture.
few common prebuilt entities such as datetimeV2, ordinal, email, and phone number.
References
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-entities
https://azure.github.io/LearnAI-DesigningandArchitectingIntelligentAgents/03-luis/1_session.html

Computer Vision – Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information. Computer Vision Detect Faces: Detect faces in an image and provide information about each detected face. Computer Vision returns the coordinates rectangle gender and age for each detected face. Computer Vision provides a subset of the Face service functionality. You can use the Face service for more detailed analysis such as facial identification and pose detection.
Use domain models to detect and identify domain-specific content in an image, such as celebrities and landmarks. For example, if an image contains people, Computer Vision can use a domain model for celebrities to determine if the people detected in the image are known celebrities.

Bing Video Search – Search for videos and get comprehensive results With Bing Video Search API v7 find videos across the web. Results provide useful metadata including creator encoding format video length view count improved & simplified paging and more.

References:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
https://azure.microsoft.com/en-us/services/cognitive-services/bing-web-search-api/

Image Classification Domains
Generic
Optimized for a broad range of image classification tasks. If none of the other domains are appropriate, or you’re unsure of which domain to choose, select the Generic domain.
Food
Optimized for photographs of dishes as you would see them on a restaurant menu. If you want to classify photographs of individual fruits or vegetables, use the Food domain.
Landmarks
Optimized for recognizable landmarks, both natural and artificial. This domain works best when the landmark is clearly visible in the photograph. This domain works even if the landmark is slightly obstructed by people in front of it.
Retail
Optimized for images that are found in a shopping catalog or shopping website. If you want high precision classifying between dresses, pants, and shirts, use this domain.
Compact domains
Optimized for the constraints of real-time classification on edge devices.
Real time classification for Food will be Food Compact.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/select-domain
https://devblogs.microsoft.com/cse/2017/05/12/food-classification-custom-vision-service/

Azure Stream Analytics (ASA) on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device-generated data. Azure Stream Analytics is designed for low latency, resiliency, efficient use of bandwidth, and compliance. Enterprises can now deploy control logic close to the industrial operations and complement Big Data analytics done in the cloud.
Azure Stream Analytics on IoT Edge runs within the Azure IoT Edge framework. Once the job is created in ASA, you can deploy and manage it using IoT Hub.

All Options with correct order:

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

Simple entity describes a single concept  like generic names like product names.
References:
https://azure.github.io/LearnAI-DesigningandArchitectingIntelligentAgents/03-luis/1_session.html

If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model. For more information about the Dispatch, including the CLI commands
References:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch?view=azure-bot-service-4.0&tabs=cs

Use Chatdown to produce prototype mock conversations in markdown and convert the markdown to transcripts you can load and view in the new V4 Bot Framework Emulator.

Train – Utterances are input from the user that your app needs to interpret. To train LUIS to extract intents and entities from them it’s important to capture a variety of different example utterances for each intent. Active learning or the process of continuing to train on new utterances is essential to machine-learned intelligence that LUIS provides.
Creating intents – Each intent needs to have example utterances at least 15. If you have an intent that does not have any example utterances you will not be able to train LUIS. If you have an intent with one or very few example utterances LUIS will not accurately predict the intent.
Never published – In each iteration of the model do not add a large quantity of utterances. Add utterances in quantities of 15. Train publish and test again.

Compact domains
The models generated by compact domains can be exported to run locally. Model performance varies by selected domain. In the table below, we report the model size and inference time on Intel Desktop CPU and NVidia GPU.

Food Classification with Custom Vision Service
In our recent engagement with Vectorform, we built a simple Android application that allows a user to obtain a food’s nutritional values based on a photo of that food. To make this scenario simpler, we will assume that the target photo has either a single food item in it or that the user will indicate the food item in question.
The main functionality of the app (image recognition) is powered by Custom Vision, where we will detect what the item is: for example, an apple or a tomato. Once we know what the food is, our goal of finding nutritional info from publicly available services is easy.
To get started, we’ll begin by classifying the following foods. For the purposes of this example, we’ll select foods that are visually distinct from each other:
Apple
Banana
Cake
Fries
Sandwich
Creating a classification model in CSV.

To demonstrate the full power of the Custom Vision classification we built, we created a mobile application on Android that would capture a food-related image, hit our endpoint to determine what food is pictured in the image, use the Nutritionix service to get nutritional information about that food, then display the results to the user.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/select-domain
https://devblogs.microsoft.com/cse/2017/05/12/food-classification-custom-vision-service/

VAIDK (Vision AI Dev Kit)
When a compact domain is selected an extra option “Export Capabilities” is provided allowing for distinguishing between “Basic Platforms” and “Vision AI Dev Kit”.
Under Export Capabilities the two options are:
1. Basic platforms (Tensorflow, CoreML, ONNX, etc.)
2. Vision AI Dev Kit.
When Vision AI Dev Kit is selected the Generic, Landmarks, and Retail but not the Food compact domains are available for Image Classification while both General (compact) and General (compact) [S1] are available for object detection.
So, Vision AI Dev Kit is not available for Food compact for image classification. so basic platforms will be selected.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/select-domain

After creating a Cognitive Service resource in the Azure portal, you’ll get an endpoint and a key for
authenticating your applications. You can access Azure Cognitive Services through two different
resources: A multi-service resource, or a single-service one.
Multi-service resource: Access multiple Azure Cognitive Services with a single key and endpoint.
Note: You need a key and endpoint for a Text Analytics resource. Azure Cognitive Services are
represented by Azure resources that you subscribe to.
Each request must include your access key and an HTTP endpoint. The endpoint specifies the region
you chose during sign up, the service URL, and a resource used on the request Box 2: 2 You need at
least one key per region.

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account

Mainly Azure Machine Learning Studio also Microsoft Visual Studio Code enable developers of the application to use a mix of Windows- and Linux-based environments. The developers can contribute to shared GitHub repositories.
References:

Due to cost reduction will use Azure blob storage instead of Data lake store
References:
https://azure.microsoft.com/en-us/services/storage/blobs/

The deployment process documented here uses one of the ARM templates to provision required resources for the bot in Azure by using the Azure CLI.
Note: When you create a bot using the Visual Studio template Yeoman template or Cookie cutter template the source code generated includes a deployment Templates folder that contains ARM templates.

The Azure Bot Service v4 SDK facilitates the development of bots that can access online resources that require authentication. Your bot does not need to manage authentication tokens. Azure does it for you using OAuth2 to generate a token, based on each user’s credentials. Your bot uses the token generated by Azure to access those resources. In this way, the user does not have to provide ID and password to the bot to access a secured resource but only to a trusted identity provider.

1- From the Azure portal configure an identity provider. The Azure Bot Service and the v4 SDK include new bot authentication capabilities providing features to make it easier to develop a bot that authenticates users to various identity providers such as Azure AD (Azure Active Directory) GitHub Uber and so on.
2- From the Azure portal create an Azure Active Directory (Azure AD) B2C service. Azure Active Directory B2C provides business-to-customer identity as a service. Your customers use their preferred social enterprise or local account identities to get single sign-on access to your applications and APIs.
3- From the Azure portal create a client application You can enable communication between your bot and your own client application by using the Direct Line API.
4-  From the bot code add the connection settings and OAuth Prompt Use an OAuth prompt to sign the user in and get a token. Azure AD B2C uses standards-based authentication protocols including OpenID Connect OAuth 2.0 and SAML.

References:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-authentication?view=azure-bot-service-4.0