HOTSPOT

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.

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Correct Answer:

Explanation:

Box 1: LAG

The 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 DURATION

Example: Compute the rate of growth, per sensor:

SELECT sensorId,

growth = reading –

LAG (reading) OVER (PARTITION BY sensorId LIMIT DURATION(hour, 1))

FROM input

Reference: 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?

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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?

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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?

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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?

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HOTSPOT

You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.

You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.

You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.

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.

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Correct Answer:

Explanation:

Box 1: Azure Stream Analytics

Box 2: Hopping

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 and be emitted more often than the window size. 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.

Box 3: Point within polygon

Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

You are designing an Azure Databricks table. The table will ingest an average of 20 million streaming events per day.

You need to persist the events in the table for use in incremental load pipeline jobs in Azure Databricks. The solution must minimize storage costs and incremental load times.

What should you include in the solution?

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HOTSPOT

You have a self-hosted integration runtime in Azure Data Factory.

The current status of the integration runtime has the following configurations:

✑ Status: Running

✑ Type: Self-Hosted

✑ Version: 4.4.7292.1

✑ Running / Registered Node(s): 1/1

✑ High Availability Enabled: False

✑ Linked Count: 0

✑ Queue Length: 0

✑ Average Queue Duration. 0.00s

The integration runtime has the following node details:

✑ Name: X-M

✑ Status: Running

✑ Version: 4.4.7292.1

✑ Available Memory: 7697MB

✑ CPU Utilization: 6%

✑ Network (In/Out): 1.21KBps/0.83KBps

✑ Concurrent Jobs (Running/Limit): 2/14

✑ Role: Dispatcher/Worker

✑ Credential Status: In Sync

Use the drop-down menus to select the answer choice that completes each statement based on the information presented. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: fail until the node comes back online

We see: High Availability Enabled: False

Note: Higher availability of the self-hosted integration runtime so that it’s no longer the single point of failure in your big data solution or cloud data integration with Data Factory.

Box 2: lowered

We see:

Concurrent Jobs (Running/Limit): 2/14

CPU Utilization: 6%

Note: When the processor and available RAM aren’t well utilized, but the execution of concurrent jobs reaches a node’s limits, scale up by increasing the number of concurrent jobs that a node can run

Reference: https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.

You need to configure workspace1 to support autoscaling all-purpose clusters.

The solution must meet the following requirements:

✑ Automatically scale down workers when the cluster is underutilized for three minutes.

✑ Minimize the time it takes to scale to the maximum number of workers.

✑ Minimize costs.

What should you do first?

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