Download Designing an Azure Data Solution.DP-201.Pass4sures.2020-01-30.87q.tqb

Premium Storage stores data on the latest technology Solid State Drives (SSDs) whereas Standard Storage stores data on Hard Disk Drives (HDDs). Premium Storage is designed for Azure Virtual Machine workloads which require consistent high IO performance and low latency in order to host IO intensive workloads like OLTP, Big Data, and Data Warehousing on platforms like SQL Server, MongoDB, Cassandra, and others. With Premium Storage, more customers will be able to lift-and-shift demanding enterprise applications to the cloud. Scenario: Traffic sensors will occasionally capture an image of a vehicle for debugging purposes.You must optimize performance of saving/storing vehicle images. The impact of vehicle images on sensor data throughout must be minimized. Reference:https://azure.microsoft.com/es-es/blog/introducing-premium-storage-high-performance-storage-for-azure-virtual-machine-workloads/

Data used for Planning Assistance must be stored in a sharded Azure SQL Database. A shard typically contains items that fall within a specified range determined by one or more attributes of the data. These attributes form the shard key (sometimes referred to as the partition key). The shard key should be static. It shouldn't be based on data that might change. Reference:https://docs.microsoft.com/en-us/azure/architecture/patterns/sharding

Azure resource costs must be minimized where possible. Data used for Planning Assistance must be stored in a sharded Azure SQL Database. The SLA for Planning Assistance is 70 percent, and multiday outages are permitted. 

Image data must be stored in a single data store at minimum cost. Note: Azure Blob storage is Microsoft's object storage solution for the cloud. Blob storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that does not adhere to a particular data model or definition, such as text or binary data.Blob storage is designed for:Serving images or documents directly to a browser. Storing files for distributed access. Streaming video and audio. Writing to log files. Storing data for backup and restore, disaster recovery, and archiving. Storing data for analysis by an on-premises or Azure-hosted service. Reference:https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction

Customer data must be analyzed using managed Spark clusters. You create spark clusters through Azure Databricks. Reference:https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal

From the scenario:Customer data must be analyzed using managed Spark clusters. All cloud data must be encrypted at rest and in transit. The solution must support: parallel processing of customer data.Reference:https://www.microsoft.com/developerblog/2019/01/18/running-parallel-apache-spark-notebook-workloads-on-azure-databricks/

CONT_SQL3 requires an initial scale of 35000 IOPS. Ultra SSD Managed Disk Offerings   The following table provides a comparison of ultra solid-state-drives (SSD) (preview), premium SSD, standard SSD, and standard hard disk drives (HDD) for managed disks to help you decide what to use.   Reference:https://docs.microsoft.com/en-us/azure/virtual-machines/windows/disks-types

The data engineers must set the SQL Data Warehouse compute resources to consume 300 DWUs. Note: There are three architectural models that are used in Azure SQL Database:General Purpose/Standard Business Critical/Premium Hyperscale Incorrect Answers:A: Business Critical service tier is designed for the applications that require low-latency responses from the underlying SSD storage (1-2 ms in average), fast recovery if the underlying infrastructure fails, or need to off-load reports, analytics, and read-only queries to the free of charge readable secondary replica of the primary database.Reference:https://docs.microsoft.com/en-us/azure/sql-database/sql-database-service-tier-business-critical

Scenario: You must be able to use a file system view of data stored in a blob. You must build an architecture that will allow Contoso to use the DB FS filesystem layer over a blob store.Databricks File System (DBFS) is a distributed file system installed on Azure Databricks clusters. Files in DBFS persist to Azure Blob storage, so you won’t lose data even after you terminate a cluster. The Databricks Delta cache, previously named Databricks IO (DBIO) caching, accelerates data reads by creating copies of remote files in nodes’ local storage using a fast intermediate data format. The data is cached automatically whenever a file has to be fetched from a remote location. Successive reads of the same data are then performed locally, which results in significantly improved reading speed. Reference:https://docs.databricks.com/delta/delta-cache.html#delta-cache

Azure SQL Data Warehouse is a cloud-based enterprise data warehouse that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution. You can access Azure SQL Data Warehouse (SQL DW) from Databricks using the SQL Data Warehouse connector (referred to as the SQL DW connector), a data source implementation for Apache Spark that uses Azure Blob Storage, and PolyBase in SQL DW to transfer large volumes of data efficiently between a Databricks cluster and a SQL DW instance. Scenario: ADatum identifies the following requirements for the Health Insights application: The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables Reference:https://docs.databricks.com/data/data-sources/azure/sql-data-warehouse.html