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Correct Answer:

Correct Answer: D  🗳️

Correct Answer:

Explanation

Correct Answer:

Explanation

Box 1: Azure Data Lake Store
Stream Analytics supports Azure Data Lake Store. Azure Data Lake Store is an enterprise-wide hyper-scale repository for big data analytic workloads. Data Lake Store enables you to store data of any size, type and ingestion speed for operational and exploratory analytics. Stream Analytics has to be authorized to access the Data Lake Store.
Box 2: Azure Cosmos DB
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.
Box 3: Azure Blob Storage
Blob storage offers a cost-effective and scalable solution for storing large amounts of unstructured data in the cloud.
Incorrect Asnwers:
Azure SQL Database:
Azure SQL Database can be used as an output for data that is relational in nature or for applications that depend on content being hosted in a relational database. Stream Analytics jobs write to an existing table in an Azure SQL Database.
Azure Service Bus Queue:
Service Bus Queues offer a First In, First Out (FIFO) message delivery to one or more competing consumers.
Typically, messages are expected to be received and processed by the receivers in the temporal order in which they were added to the queue, and each message is received and processed by only one message consumer.
Azure Table Storage
Azure Table storage offers highly available, massively scalable storage, so that an application can automatically scale to meet user demand. Table storage is Microsoft's NoSQL key/attribute store, which one can leverage for structured data with fewer constraints on the schema. Azure Table storage can be used to store data for persistence and efficient retrieval.
References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

Correct Answer:

Explanation

From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId, SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.