Question # 1 You plan to create an Azure Synapse Analytics dedicated SQL pool. You need to minimize the time it takes to identify queries that return confidential information as defined by the company's data privacy regulations and the users who executed the queues. Which two components should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
sensitivity-classification labels applied to columns that contain confidential information
resource tags for databases that contain confidential information
audit logs sent to a Log Analytics workspace
dynamic data masking for columns that contain confidential information
Click for Answer
sensitivity-classification labels applied to columns that contain confidential information
audit logs sent to a Log Analytics workspace
Answer Description A: You can classify columns manually, as an alternative or in addition to the recommendation-based classification:
Question # 2 What should you do to improve high availability of the real-time data processing solution?
Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
Deploy a High Concurrency Databricks cluster.
Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
Set Data Lake Storage to use geo-redundant storage (GRS).
Click for Answer
Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
Answer Description Explanation: Guarantee Stream Analytics job reliability during service updates Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model. Scenario: The application development team will create an Azure event hub to receive realtime sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability
Question # 3 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?
From ADFdev, modify the Git configuration.
From ADFdev, create a linked service.
From Azure DevOps, create a release pipeline.
From Azure DevOps, update the main branch.
Click for Answer
From Azure DevOps, create a release pipeline.
Answer Description Explanation: In Azure Data Factory, continuous integration and delivery (CI/CD) means moving Data Factory pipelines from one environment (development, test, production) to another. Note: The following is a guide for setting up an Azure Pipelines release that automates the deployment of a data factory to multiple environments. In Azure DevOps, open the project that's configured with your data factory. On the left side of the page, select Pipelines, and then select Releases. Select New pipeline, or, if you have existing pipelines, select New and then New release pipeline. In the Stage name box, enter the name of your environment. Select Add artifact, and then select the git repository configured with your development data factory. Select the publish branch of the repository for the Default branch. By default, this publish branch is adf_publish. Select the Empty job template. Reference:
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