Download Microsoft.DP-201.TestKing.2019-04-27.31q.vcex

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. References: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. References:https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal

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.     References: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. References: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. References:https://docs.databricks.com/delta/delta-cache.html#delta-cache

Depending on what services and workloads are using the data, a good size to consider for files is 256 MB or greater. If the file sizes cannot be batched when landing in Data Lake Storage Gen1, you can have a separate compaction job that combines these files into larger ones. Note: 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

Vertical partitioning is used for cross-database queries. Instead we should use Horizontal Partitioning, which also is called charding. References:https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

We should use Horizontal Partitioning through Sharding, not divide through regions. Note: Horizontal Partitioning - Sharding: Data is partitioned horizontally to distribute rows across a scaled out data tier. With this approach, the schema is identical on all participating databases. This approach is also called “sharding”. Sharding can be performed and managed using (1) the elastic database tools libraries or (2) self-sharding. An elastic query is used to query or compile reports across many shards. References:https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

Horizontal Partitioning - Sharding: Data is partitioned horizontally to distribute rows across a scaled out data tier. With this approach, the schema is identical on all participating databases. This approach is also called “sharding”. Sharding can be performed and managed using (1) the elastic database tools libraries or (2) self-sharding. An elastic query is used to query or compile reports across many shards. References:https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

For large graphs with lots of entities and relationships, you can perform very complex analyses very quickly. Many graph databases provide a query language that you can use to traverse a network of relationships efficiently. Relevant Azure service: Cosmos DBReferences:https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview