A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream Analytics cloud job to analyze the data. The cloud job is configured to use 120 Streaming Units (SU).
You need to optimize performance for the Azure Stream Analytics job.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A.Implement event ordering.
• B.Implement Azure Stream Analytics user-defined functions (UDF).
• C.Implement query parallelization by partitioning the data output.
• D.Scale the SU count for the job up.
• E.Scale the SU count for the job down.
• F.Implement query parallelization by partitioning the data input.

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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?

• A.snapshot
• B.tumbling
• C.hopping
• D.sliding

Correct Answer: B

Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

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

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You have the following Azure Data Factory pipelines
* ingest Data from System 1
* Ingest Data from System2
* Populate Dimensions
* Populate facts
ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours.
What should you do to schedule the pipelines for execution?

• A.Add an event trigger to all four pipelines.
• B.Create a parent pipeline that contains the four pipelines and use an event trigger.
• C.Create a parent pipeline that contains the four pipelines and use a schedule trigger.
• D.Add a schedule trigger to all four pipelines.

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You are designing database for an Azure Synapse Analytics dedicated SQL pool to support workloads for detecting ecommerce transaction fraud.
Data will be combined from multiple ecommerce sites and can include sensitive financial information such as credit card numbers.
You need to recommend a solution that meets the following requirements:
* Users must be able to identify potentially fraudulent transactions.
* Users must be able to use credit cards as a potential feature in models.
* Users must NOT be able to access the actual credit card numbers.
What should you include in the recommendation?

• A.column-level encryption
• B.Azure Active Directory (Azure AD) pass-through authentication
• C.row-level security (RLS)
• D.Transparent Data Encryption (TDE)

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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?

• A.From ADFdev, modify the Git configuration.
• B.From ADFdev, create a linked service.
• C.From Azure DevOps, create a release pipeline.
• D.From Azure DevOps, update the main branch.

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