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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). 
References:
http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing

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

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

Azure HDInsight makes it easy, fast, and cost-effective to process massive amounts of data. 
You can use HDInsight to process streaming data that's received in real time from a variety of devices. 
References:
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-introduction

Instead add an SSH key to the node, and then you create an SSH connection. 
References:
https://docs.microsoft.com/en-us/azure/aks/ssh

With Azure Data Factory you can use workflows to orchestrate data integration and data transformation processes at scale. 
Build data integration, and easily transform and integrate big data processing and machine learning with the visual interface. 
References:
https://azure.microsoft.com/en-us/services/data-factory/

You can use HDInsight to process streaming data that's received in real time from a variety of devices. 
Internet of Things (IoT) 
You can use HDInsight to build applications that extract critical insights from data. You can also use Azure Machine Learning on top of that to predict future trends for your business. 
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. 
References:
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-introduction

Generally, 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). Data Lake is priced on volume, so it will go up as you reach certain tiers of volume. 
References:
http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing