Download Designing and Implementing an Azure AI Solution.AI-100.GratisExam.2020-08-27.87q.tqb

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 (SQL Server 2016 R Services or SQL Server 2017 MachineLearning 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

When an application needs access to deploy or configure resources through Azure Resource Manager in Azure Stack, you create a service principal, which is a credential for your application. You can then delegate only the necessary permissions to that service principal. References:https://docs.microsoft.com/en-us/azure/azure-stack/azure-stack-create-service-principals

FPGAs, such as those available on Azure, provide performance close to ASICs. They are also flexible and reconfigurable over time, to implement new logic. Incorrect Answers:D: ASICs are custom circuits, such as Google's TensorFlow Processor Units (TPU), provide the highest efficiency. They can't be reconfigured as your needs change. References:https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas

An AKS cluster has one or more nodes. References:https://docs.microsoft.com/en-us/azure/aks/concepts-clusters-workloads