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A company installs IoT devices to monitor its fleet of delivery vehicles. Data from devices is collected from Azure Event Hub. The data must be transmitted to Power BI for real-time data visualizations. You need to recommend a solution. What should you recommend?
Azure HDInsight with Spark Streaming
Apache Spark in Azure Databricks
Azure Stream Analytics
Azure HDInsight with Storm

Azure Stream Analytics

Explanation
Step 1: Get your IoT hub ready for data access by adding a consumer group.
Step 2: Create, configure, and run a Stream Analytics job for data transfer from your IoT hub to your Power BI
account.
Step 3: Create and publish a Power BI report to visualize the data.
References:
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-live-data-visualization-in-power-bi

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are designing an HDInsight/Hadoop cluster solution that uses Azure Data Lake Gen1 Storage. The solution requires POSIX permissions and enables diagnostics logging for auditing. You need to recommend solutions that optimize storage. Proposed Solution: Ensure that files stored are smaller than 250MB. Does the solution meet the goal?
Yes
No

No

Explanation
Ensure that files stored are larger, not smaller than 250MB.
You can have a separate compaction job that combines these files into larger ones.
Note: The file POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that
becomes apparent when working with numerous small files. As a best practice, you must batch your data into
larger files versus writing thousands or millions of small files to Data Lake Storage Gen1. Avoiding small file
sizes can have multiple benefits, such as:
Lowering the authentication checks across multiple files
Reduced open file connections
Faster copying/replication
Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions
References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

You are designing a real-time stream solution based on Azure Functions. The solution will process data uploaded to Azure Blob Storage. The solution requirements are as follows: New blobs must be processed with a little delay as possible. Scaling must occur automatically. Costs must be minimized. What should you recommend?
Deploy the Azure Function in an App Service plan and use a Blob trigger.
Deploy the Azure Function in a Consumption plan and use an Event Grid trigger.
Deploy the Azure Function in a Consumption plan and use a Blob trigger.
Deploy the Azure Function in an App Service plan and use an Event Grid trigger

Deploy the Azure Function in a Consumption plan and use a Blob trigger.

Explanation
Create a function, with the help of a blob trigger template, which is triggered when files are uploaded to or
updated in Azure Blob storage.
You use a consumption plan, which is a hosting plan that defines how resources are allocated to your function
app. In the default Consumption Plan, resources are added dynamically as required by your functions. In this
serverless hosting, you only pay for the time your functions run. When you run in an App Service plan, you
must manage the scaling of your function app.
References:
https://docs.microsoft.com/en-us/azure/azure-functions/functions-create-storage-blob-triggered-function

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