HOTSPOT (Drag and Drop is not supported)You have an Azure subscription.You need to deploy a solution that will provide point-in-time restore for blobs in storage accounts that have blob versioning and blob soft delete enabled.Which type of blob should you create, and what should you enable for the accounts? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: BlockPoint-in-time restore for block blobsPoint-in-time restore provides protection against accidental deletion or corruption by enabling you to restore block blob data to an earlier state. Point-in-time restore is useful in scenarios where a user or application accidentally deletes data or where an application error corrupts data. Point-in-time restore also enables testing scenarios that require reverting a data set to a known state before running further tests.Point-in-time restore is supported for general-purpose v2 storage accounts in the standard performance tier only. Only data in the hot and cool access tiers can be restored with point-in-time restore.Box 2: The change feedEnable and configure point-in-time restoreBefore you enable and configure point-in-time restore, enable its prerequisites for the storage account: soft delete, change feed, and blob versioning.
https://learn.microsoft.com/en-us/azure/storage/blobs/point-in-time-restore-overview
HOTSPOT (Drag and Drop is not supported)Your company, named Contoso, Ltd., has an Azure subscription that contains the following resources:An Azure Synapse Analytics workspace named contosoworkspace1An Azure Data Lake Storage account named contosolake1An Azure SQL database named contososql1The product data of Contoso is copied from contososql1 to contosolake1.Contoso has a partner company named Fabrikam Inc. Fabrikam has an Azure subscription that contains the following resources:A virtual machine named FabrikamVM1 that runs Microsoft SQL Server 2019An Azure Storage account named fabrikamsa1Contoso plans to upload the research data on FabrikamVM1 to contosolake1. During the upload, the research data must be transformed to the data formats used by Contoso.The data in contosolake1 will be analyzed by using contosoworkspace1.You need to recommend a solution that meets the following requirements:Upload and transform the FabrikamVM1 research data.Provide Fabrikam with restricted access to snapshots of the data in contosoworkspace1.What should you recommend for each requirement? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: Azure Synapse pipelinesUpload and transform the FabrikamVM1 research data.The lake database in Azure Synapse Analytics enables customers to bring together database design, meta information about the data that is stored and a possibility to describe how and where the data should be stored.Box 2: Azure Data ShareProvide Fabrikam with restricted access to snapshots of the data in contosoworkspace1.Azure Data Share enables organizations to securely share data with multiple customers and partners. Data providers are always in control of the data that they’ve shared and Azure Data Share makes it simple to manage and monitor what data was shared, when and by whom.Azure Data Share currently offers snapshot-based sharing and in-place sharing.Incorrect:* Azure Data Box GatewayAzure Data Box Gateway is a storage solution that enables you to seamlessly send data to Azure.Use casesData Box Gateway can be leveraged for transferring data to the cloud such as cloud archival, disaster recovery, or if there is a need to process your data at cloud scale. Here are the various scenarios where Data Box Gateway can be used for data transfer.Cloud archival – Copy hundreds of TBs of data to Azure storage using Data Box Gateway in a secure and efficient manner. The data can be ingested one time or an ongoing basis for archival scenarios.Continuous data ingestion – Continuously ingest data into the device to copy to the cloud, regardless of the data size. As the data is written to the gateway device, the device uploads the data to Azure Storage.Initial bulk transfer followed by incremental transfer – Use Data Box for the bulk transfer in an offline mode (initial seed) and Data Box Gateway for incremental transfers (ongoing feed) over the network.
https://learn.microsoft.com/en-us/azure/synapse-analytics/database-designer/concepts-lake-database https://learn.microsoft.com/en-us/azure/data-share/overview
HOTSPOT (Drag and Drop is not supported)You are designing a data pipeline that will integrate large amounts of data from multiple on-premises Microsoft SQL Server databases into an analytics platform in Azure. The pipeline will include the following actions:Database updates will be exported periodically into a staging area in Azure Blob storage.Data from the blob storage will be cleansed and transformed by using a highly parallelized load process.The transformed data will be loaded to a data warehouse.Each batch of updates will be used to refresh an online analytical processing (OLAP) model in a managed serving layer.The managed serving layer will be used by thousands of end users.You need to implement the data warehouse and serving layers.What should you use? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: An Azure Synapse Analytics dedicated SQL poolData warehouseAzure Synapse Analytics is an analytics service that brings together enterprise data warehousing and Big Data analytics. Dedicated SQL pool (formerly SQL DW) refers to the enterprise data warehousing features that are available in Azure Synapse Analytics.Dedicated SQL pool (formerly SQL DW) represents a collection of analytic resources that are provisioned when using Synapse SQL. The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).Once your dedicated SQL pool is created, you can import big data with simple PolyBase T-SQL queriesNote: The massively parallel processing architecture of Azure Synapse provides scalability and high performance.Example:Data warehousing and analyticsThis example scenario demonstrates a data pipeline that integrates large amounts of data from multiple sources into a unified analytics platform in Azure.ArchitectureDataflowThe data flows through the solution as follows:1. For each data source, any updates are exported periodically into a staging area in Azure Data Lake Storage.2. Azure Data Factory incrementally loads the data from Azure Data Lake Storage into staging tables in Azure Synapse Analytics. The data is cleansed and transformed during this process. PolyBase can parallelize the process for large datasets.3. After loading a new batch of data into the warehouse, a previously created Azure Analysis Services tabular model is refreshed. This semantic model simplifies the analysis of business data and relationships.4. Business analysts use Microsoft Power BI to analyze warehoused data via the Analysis Services semantic model.Box 2: Azure Analysis ServicesServing layerEach batch of updates will be used to refresh an online analytical processing (OLAP) model in a managed serving layer.The managed serving layer will be used by thousands of end users.
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview- what-is https://learn.microsoft.com/en-us/azure/architecture/example-scenario/data/data-warehouse
HOTSPOT (Drag and Drop is not supported)You have an Azure subscription.You need to deploy a relational database. The solution must meet the following requirements:Support multiple read-only replicas.Automatically load balance read-only requests across all the read-only replicas.Minimize administrative effortWhat should you use? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: A single Azure SQL databaseServiceHyperscale allows for 1-4 secondaries (1 by default).If more than one secondary replica is present, the workload is distributed across all available secondaries.Note: As part of High Availability architecture, each single database or elastic pool database in the Premium and Business Critical service tier is automatically provisioned with a primary read-write replica and one or more secondary read-only replicas. The secondary replicas are provisioned with the same compute size as the primary replica. The read scale-out feature allows you to offload read-only workloads using the compute capacity of one of the read-only replicas, instead of running them on the read-write replica. This way, some read-only workloads can be isolated from the read-write workloads, and don’t affect their performance. The feature is intended for the applications that include logically separated read-only workloads, such as analytics. In the Premium and Business Critical service tiers, applications could gain performance benefits using this additional capacity at no extra cost.Box 2: HyperscaleService tierIn Premium and Business Critical service tiers, only one of the read-only replicas is accessible at any given time. Hyperscale supports multiple read-only replicas.
https://learn.microsoft.com/en-us/azure/azure-sql/database/read-scale-out?view=azuresql
You have an app named App1 that uses an Azure Blob Storage container named app1data.App1 uploads a cumulative transaction log file named File1.txt to a block blob in app1data once every hour. File1.txt only stores transaction data from the current day.You need to ensure that you can restore the last uploaded version of File1.txt from any day for up to 30 days after the file was overwritten. The solution must minimize storage space.What should you include in the solution?
Answer(s): D
You can enable Blob storage versioning to automatically maintain previous versions of a blob when it is modified or deleted. When blob versioning is enabled, then you can restore an earlier version of a blob to recover your data if it is erroneously modified or deleted.Note: To enable blob versioning for a storage account in the Azure portal:1. Navigate to your storage account in the portal.2. Under Data management, choose Data protection.3. In the Tracking section, select Enable versioning for blobs, and then choose whether to keep all versions or delete them after a period of time.Incorrect:Not B: Blob versioning and blob snapshotsA blob snapshot is a read-only copy of a blob that’s taken at a specific point in time. Blob snapshots and blob versions are similar, but a snapshot is created manually by you or your application, while a blob version is created automatically on a write or delete operation when blob versioning is enabled for your storage account.
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