HOTSPOT (Drag and Drop is not supported)You are designing a real-time dashboard solution that will visualize streaming data from remote sensors that connect to the internet. The streaming data must be aggregated to show the average value of each 10-second interval. The data will be discarded after being displayed in the dashboard.The solution will use Azure Stream Analytics and must meet the following requirements: -Minimize latency from an Azure Event hub to the dashboard.-Minimize the required storage.-Minimize development effort.What should you include in the solution? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-power-bi-dashboard
DRAG DROP (Drag and Drop is not supported)You have an Azure Stream Analytics job that is a Stream Analytics project solution in Microsoft Visual Studio. The job accepts data generated by IoT devices in the JSON format.You need to modify the job to accept data generated by the IoT devices in the Protobuf format.Which three actions should you perform from Visual Studio on sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
Step 1: Add an Azure Stream Analytics Custom Deserializer Project (.NET) project to the solution. Create a custom deserializer1. Open Visual Studio and select File > New > Project. Search for Stream Analytics and select Azure Stream Analytics Custom Deserializer Project (.NET). Give the project a name, like Protobuf Deserializer.2. In Solution Explorer, right-click your Protobuf Deserializer project and select Manage NuGet Packages from the menu. Then install the Microsoft.Azure.StreamAnalytics and Google.Protobuf NuGet packages.3. Add the MessageBodyProto class and the MessageBodyDeserializer class to your project.4. Build the Protobuf Deserializer project.Step 2: Add .NET deserializer code for Protobuf to the custom deserializer projectAzure Stream Analytics has built-in support for three data formats: JSON, CSV, and Avro. With custom .NET deserializers, you can read data from other formats such as Protocol Buffer, Bond and other user defined formats for both cloud and edge jobs.Step 3: Add an Azure Stream Analytics Application project to the solution Add an Azure Stream Analytics projectIn Solution Explorer, right-click the Protobuf Deserializer solution and select Add > New Project. Under Azure Stream Analytics > Stream Analytics, choose Azure Stream Analytics Application. Name it ProtobufCloudDeserializer and select OK.Right-click References under the ProtobufCloudDeserializer Azure Stream Analytics project. Under Projects, add Protobuf Deserializer. It should be automatically populated for you.
https://docs.microsoft.com/en-us/azure/stream-analytics/custom-deserializer
You have an Azure Storage account and a data warehouse in Azure Synapse Analytics in the UK South region. You need to copy blob data from the storage account to the data warehouse by using Azure Data Factory. The solution must meet the following requirements:-Ensure that the data remains in the UK South region at all times. -Minimize administrative effort.Which type of integration runtime should you use?
Incorrect Answers:C: Self-hosted integration runtime is to be used On-premises.
https://docs.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime
HOTSPOT (Drag and Drop is not supported)You have an Azure SQL database named Database1 and two Azure event hubs named HubA and HubB. The data consumed from each source is shown in the following table.You need to implement Azure Stream Analytics to calculate the average fare per mile by driver.How should you configure the Stream Analytics input for each source? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
HubA: Stream HubB: StreamDatabase1: ReferenceReference data (also known as a lookup table) is a finite data set that is static or slowly changing in nature, used to perform a lookup or to augment your data streams. For example, in an IoT scenario, you could store metadata about sensors (which don’t change often) in reference data and join it with real time IoT data streams. Azure Stream Analytics loads reference data in memory to achieve low latency stream processing
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
You have an Azure Stream Analytics job that receives clickstream data from an Azure event hub.You need to define a query in the Stream Analytics job. The query must meet the following requirements: Count the number of clicks within each 10-second window based on the country of a visitor.Ensure that each click is NOT counted more than once. How should you define the Query?
Answer(s): B
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.Example:Incorrect Answers:A: Sliding windows, unlike Tumbling or Hopping windows, output events only for points in time when the content of the window actually changes. In other words, when an event enters or exits the window. Every window has at least one event, like in the case of Hopping windows, events can belong to more than one sliding window.C: Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.D: Session windows group events that arrive at similar times, filtering out periods of time where there is no data.
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
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