Note: This question is part of series of questions that present the same scenario. Each question in the series contain a unique solution. Determine whether the solution meets the stated goals.
You develop data engineering solutions for a company.
A project requires the deployment of resources to Microsoft Azure for batch data processing on Azure HDInsight. Batch processing will run daily and must:
Scale to minimize costs
Be monitored for cluster performance
You need to recommend a tool that will monitor clusters and provide information to suggest how to scale.
Solution: Download Azure HDInsight cluster logs by using Azure PowerShell.
Does the solution meet the goal?

• A.Yes
• B.No

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You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html

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You implement an enterprise data warehouse in Azure Synapse Analytics.
You have a large fact table that is 10 terabytes (TB) in size.
Incoming queries use the primary key Sale Key column to retrieve data as displayed in the following table:

You need to distribute the large fact table across multiple nodes to optimize performance of the table.
Which technology should you use?

• A.hash distributed table with clustered ColumnStore index
• B.hash distributed table with clustered index
• C.heap table with distribution replicate
• D.round robin distributed table with clustered index
• E.round robin distributed table with clustered ColumnStore index

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Note: This question is part of series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop data engineering solutions for a company.
A project requires the deployment of resources to Microsoft Azure for batch data processing on Azure HDInsight. Batch processing will run daily and must:
* Scale to minimize costs
* Be monitored for cluster performance
You need to recommend a tool that will monitor clusters and provide information to suggest how to scale.
Solution: Monitor clusters by using Azure Log Analytics and HDInsight cluster management solutions.
Does the solution meet the goal?

• A.Yes
• B.No

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Name: *

Email: *

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You have an Azure Stream Analytics job that is a Stream Analytics project solution in Microsoft Visual Studio. The job accepts data generated by IoT devices in the JSON format.
You need to modify the job to accept data generated by the IoT devices in the Protobuf format.
Which three actions should you perform from Visual Studio in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/custom-deserializer

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