How should you configure the Stream Analytics input for each source?

HOTSPOT

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.

Answer:

Explanation:

HubA: Stream

HubB: Stream

Database1: Reference

Reference 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

Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data

9 1. 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?
A . SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SlidingWindow(second, 10)
B . SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, TumblingWindow(second, 10)
C . SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, HoppingWindow(second, 10, 2)
D . SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SessionWindow(second, 5, 10)

View Answer

Answer: B

Explanation:

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:

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