What High Quality DP-201 Exam Engine Is

As of now, the cost of running an Azure SQL database instance is based on the number of Database Throughput Units (DTUs) allocated for the database. When determining the number of units to allocate for the
solution, a major contributing factor is to identify what processing power is needed to handle the volume of expected requests.
Running the statement to upgrade/downgrade your database takes a matter of seconds.

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Azure Files enables you to set up highly available network file shares that can be accessed by using the standard Server Message Block (SMB) protocol.
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-introduction https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview

To implement a lambda architecture on Azure, you can combine the following technologies to accelerate realtime big data analytics:
Azure Cosmos DB, the industry's first globally distributed, multi-model database service.
Apache Spark for Azure HDInsight, a processing framework that runs large-scale data analytics applications
Azure Cosmos DB change feed, which streams new data to the batch layer for HDInsight to process The Spark to Azure Cosmos DB Connector
E: You can use Apache Spark to stream data into or out of Apache Kafka on HDInsight using DStreams. References:
https://docs.microsoft.com/en-us/azure/cosmos-db/lambda-architecture

User-Defined Restore Points
This feature enables you to manually trigger snapshots to create restore points of your data warehouse before and after large modifications. This capability ensures that restore points are logically consistent, which provides additional data protection in case of any workload interruptions or user errors for quick recovery time.
Note: A data warehouse restore is a new data warehouse that is created from a restore point of an existing or deleted data warehouse. Restoring your data warehouse is an essential part of any business continuity and disaster recovery strategy because it re-creates your data after accidental corruption or deletion.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore

Ensure that files stored are larger, not smaller than 250MB.
You can have a separate compaction job that combines these files into larger ones.
Note: The file 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

References:
https://cloudblogs.microsoft.com/sqlserver/2018/12/17/confidential-computing-using-always-encrypted-withsec

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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

To achieve the fastest loading speed for moving data into a data warehouse table, load data into a staging table. Define the staging table as a heap and use round-robin for the distribution option.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

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) selfsharding.
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

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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

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.

If your storage account has GRS enabled, then your data is durable even in the case of a complete regional outage or a disaster in which the primary region isn’t recoverable.
References:
https://medium.com/microsoftazure/data-durability-fault-tolerance-resilience-in-azure-databricks- 95392982bac7

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Spark in SQL Server big data cluster enables AI and machine learning.
You can use Apache Spark MLlib to create a machine learning application to do simple predictive analysis on an open dataset.
MLlib is a core Spark library that provides many utilities useful for machine learning tasks, including utilities that are suitable for:
Classification
Regression
Clustering
Topic modeling
Singular value decomposition (SVD) and principal component analysis (PCA)
Hypothesis testing and calculating sample statistics
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-machine-learning-mllib-ipython

SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price.
You can configure resources for the pool based either on the DTU-based purchasing model or the vCorebased purchasing model.

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