Topic 1 · Question 175

B and D are correct. In the statement we can read : « deploy several million sensors that will upload data to Azure ». Here the keyword is deploy. To deploy IoT solutions you have to use Azure IoT Hub. The Data Lake is for analytics not deployment. You will obviously use the Queue Storage to handle the amount of exchanges.

In my opinion Queue is correct,
"Azure Queue Storage is a service for storing large numbers of messages. You access messages from anywhere in the world via authenticated calls using HTTP or HTTPS. A queue message can be up to 64 KB in size. A queue may contain millions of messages, up to the total capacity limit of a storage account. Queues are commonly used to create a backlog of work to process asynchronously."

source "https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction"

"The sensors will upload DATA" it doesn't says messages or logs.

"The sensors will upload DATA" it doesn't says messages or logs.

Datalake is used to process large amount of data and perform analytics on top of that. It is not used by transaction purpose. Once you get IoT data in queue, you can push it to datalake for further analysis purpose. so queue is right answer.

OK, I hope a question like this doesn't come up as I can't decide if it should be Queue storage or Data Lake!

Queues are for sequencial processing of messeges (requests) and doesn't store data for long. Once a request is processed it is then removed from the queue. The data uploaded by the sensors don't need to be processed immediately and should be stored for a good period of time to allow it to be analysed later. Hence Data Lake is more appropriate answer. Even if you implement a queue, the data will still end up in Data Lake or other kind of databases.

A&D is correct

Queues are for sequencial processing of messeges (requests) and doesn't store data for long. Once a request is processed it is then removed from the queue. The data uploaded by the sensors don't need to be processed immediately and should be stored for a good period of time to allow it to be analysed later. Hence Data Lake is more appropriate answer. Even if you implement a queue, the data will still end up in Data Lake or other kind of databases.

Queues are for sequencial processing of messeges (requests) and doesn't store data for long. Once a request is processed it is then removed from the queue. The data uploaded by the sensors don't need to be processed immediately and should be stored for a good period of time to allow it to be analysed later. Hence Data Lake is more appropriate answer. Even if you implement a queue, the data will still end up in Data Lake or other kind of databases.

Queues are best suited for fail-safe transactional data log keeping. Lets say you are an E-Commerce company, for some reason, your SQL database isn't available in such situation you have a fail-safe mechanism to transfer transactional data to Azure Queues , which allows transactions to be saved and resumed for operational use when SQL storage is available.

Correct. We know that IOT hub is one of the correct answer. The answer is Data Lake, specifically from the link above.. IOT hub can store data at two places, and hence data lakes appears to be the correct second choice, depending on the volume of the data.

Azure Storage as a routing endpoint
There are two storage services IoT Hub can route messages to -- Azure Blob Storage and Azure Data Lake Storage Gen2 (ADLS Gen2) accounts.

Queues are best suited for fail-safe transactional data log keeping. Lets say you are an E-Commerce company, for some reason, your SQL database isn't available in such situation you have a fail-safe mechanism to transfer transactional data to Azure Queues , which allows transactions to be saved and resumed for operational use when SQL storage is available.