HOTSPOT (Drag and Drop is not supported)You are building an Azure Analytics query that will receive input data from Azure IoT Hub and write the results to Azure Blob storage. You need to calculate the difference in readings per sensor per hour.How should you complete the query? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: LAGThe LAG analytic operator allows one to look up a “previous” event in an event stream, within certain constraints. It is very useful for computing the rate of growth of a variable, detecting when a variable crosses a threshold, or when a condition starts or stops being true.Box 2: LIMIT DURATIONExample: Compute the rate of growth, per sensor:SELECT sensorId, growth = reading -LAG(reading) OVER (PARTITION BY sensorId LIMIT DURATION(hour, 1))FROM input
https://docs.microsoft.com/en-us/stream-analytics-query/lag-azure-stream-analytics
You need to schedule an Azure Data Factory pipeline to execute when a new file arrives in an Azure Data Lake Storage Gen2 container. Which type of trigger should you use?
Answer(s): D
Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require Data Factory customers to trigger pipelines based on events happening in storage account, such as the arrival or deletion of a file in Azure Blob Storage account.
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-event-trigger
You have two Azure Data Factory instances named ADFdev and ADFprod. ADFdev connects to an Azure DevOps Git repository. You publish changes from the main branch of the Git repository to ADFdev.You need to deploy the artifacts from ADFdev to ADFprod. What should you do first?
Answer(s): C
In Azure Data Factory, continuous integration and delivery (CI/CD) means moving Data Factory pipelines from one environment (development, test, production) to another.Note:The following is a guide for setting up an Azure Pipelines release that automates the deployment of a data factory to multiple environments.In Azure DevOps, open the project that's configured with your data factory. On the left side of the page, select Pipelines, and then select Releases.Select New pipeline, or, if you have existing pipelines, select New and then New release pipeline. In the Stage name box, enter the name of your environment.Select Add artifact, and then select the git repository configured with your development data factory. Select the publish branch of the repository for the Default branch. By default, this publish branch is adf_publish.Select the Empty job template.
https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-deployment
You are developing a solution that will stream to Azure Stream Analytics. The solution will have both streaming data and reference data. Which input type should you use for the reference data?
Answer(s): B
Stream Analytics supports Azure Blob storage and Azure SQL Database as the storage layer for Reference Data.
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals. Which type of window should you use?
Unlike tumbling windows, hopping windows model scheduled overlapping windows. A hopping window specification consist of three parameters: the timeunit, the windowsize (how long each window lasts) and the hopsize (by how much each window moves forward relative to the previous one).
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
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