Download Microsoft.AI-100.PrepAway.2021-02-17.156q.vcex

Box 1: Azure Cognitive ServicesAzure Cognitive Services include image-processing algorithms to smartly identify, caption, index, and moderate your pictures and videos. Not: Azure Linguistic Analytics API, which provides advanced natural language processing over raw text.Box 2: Azure Data FactoryThe Azure Data Factory (ADF) is a service designed to allow developers to integrate disparate data sources.  It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud. It provides access to on-premises data in SQL Server and cloud data in Azure Storage (Blob and Tables) and Azure SQL Database. References:https://azure.microsoft.com/en-us/services/cognitive-services/https://www.jamesserra.com/archive/2014/11/what-is-azure-data-factory/

Box 1: Self-hosted Integration RuntimeA self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network. Not Azure-SSIS Integration Runtime, as you would need to write custom code. Box 2: Azure Logic AppsAzure Logic Apps helps you orchestrate and integrate different services by providing 100+ ready-to-use connectors, ranging from on-premises SQL Server or SAP to Microsoft Cognitive Services. Incorrect:Not Azure API Management: Use Azure API Management as a turnkey solution for publishing APIs to external and internal customers. References:https://docs.microsoft.com/en-us/azure/data-factory/concepts-integration-runtimehttps://docs.microsoft.com/en-us/azure/logic-apps/logic-apps-examples-and-scenarios

Box 1: Azure Bot ServiceBox 2: Computer VisionThe 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/cognitive-services/computer-vision/

Data Lake will be a bit more expensive although they are in close range of each other. Blob storage has more options for pricing depending upon things like how frequently you need to access your data (cold vs hot storage). Reference:http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing

Table Storage is a NoSQL key-value store for rapid development using massive semi-structured datasets You can develop applications on Cosmos DB using popular NoSQL APIs. Both services have a different scenario and pricing model. While Azure Storage Tables is aimed at high capacity on a single region (optional secondary read only region but no failover), indexing by PK/RK and storage-optimized pricing; Azure Cosmos DB Tables aims for high throughput (single-digit millisecond latency), global distribution (multiple failover), SLA-backed predictive performance with automatic indexing of each attribute/property and a pricing model focused on throughput. References:https://db-engines.com/en/system/Microsoft+Azure+Cosmos+DB%3BMicrosoft+Azure+Table+Storage

The process of securing a new web service or an existing one is as follows:Get a domain name. Get a digital certificate. Deploy or update the web service with the SSL setting enabled. Update your DNS to point to the web service. Note: To deploy (or re-deploy) the service with SSL enabled, set the ssl_enabled parameter to True, wherever applicable. Set the ssl_certificate parameter to the value of the certificate file and the ssl_key to the value of the key file. References:https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service

References:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage

References:https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/studio/algorithm-choice.md

B: To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues are detected, the number of nodes is increased to meet the application demand. Nodes are also regularly checked for a lack of running pods, with the number of nodes then decreased as needed. This ability to automatically scale up or down the number of nodes in your AKS cluster lets you run an efficient, cost-effective cluster. A: You can also use the horizontal pod autoscaler to automatically adjust the number of pods that run your application. Reference:https://docs.microsoft.com/en-us/azure/aks/cluster-autoscaler

Remote computing is available for specific data sources on selected platforms. The following tables document the supported combinations. RxInSqlServer, sqlserver: Remote compute context. Target server is a single database node (SQLServer 2016 R Services or SQL Server 2017 Machine Learning Services). Computation is parallel, but not distributed. RxSpark, spark: Remote compute context. Target is a Spark cluster on Hadoop.RxLocalParallel, localpar: Compute context is often used to enable controlled, distributed computations relying on instructions you provide rather than a built-in scheduler on Hadoop. You can use compute context for manual distributed computing. References:https://docs.microsoft.com/en-us/machine-learning-server/r/concept-what-is-compute-context