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Free PDF DP-203 - Data Engineering on Microsoft Azure–High-quality Valid Test Fee

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The Data Engineering on Microsoft Azure (DP-203) mock exams will allow you to prepare for the DP-203 exam in a smarter and faster way. You can improve your understanding of the DP-203 exam objectives and concepts with the easy-to-understand and actual DP-203 Exam Questions offered by DumpsTorrent. DumpsTorrent makes the DP-203 Practice Questions affordable for everyone and allows you to find all the information you need to polish your skills to be completely ready to clear the DP-203 exam on the first attempt.

The DP-203 Exam covers a wide range of topics related to data engineering on Azure, including data ingestion and processing, data storage, data transformation, and data security. Candidates are expected to have a deep understanding of Azure services, such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics, as well as knowledge of programming languages such as SQL, Python, and Scala.

The DP-203 exam is intended for professionals with a strong foundation in data engineering and experience working with Azure data services. Data Engineering on Microsoft Azure certification is ideal for data engineers, data architects, and database administrators who want to expand their skill set and gain expertise in designing and implementing data solutions on Azure. Data Engineering on Microsoft Azure certification exam covers a wide range of topics, including data integration, data storage, data processing, and data security, and it requires a thorough understanding of Azure services and tools.

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q211-Q216):

NEW QUESTION # 211
You have a SQL pool in Azure Synapse.
You plan to load data from Azure Blob storage to a staging table. Approximately 1 million rows of data will be loaded daily. The table will be truncated before each daily load.
You need to create the staging table. The solution must minimize how long it takes to load the data to the staging table.
How should you configure the table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Graphical user interface, application, table Description automatically generated

Box 1: Hash
Hash-distributed tables improve query performance on large fact tables. They can have very large numbers of rows and still achieve high performance.
Box 2: Clustered columnstore
When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed.
Box 3: Date
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
Partition switching can be used to quickly remove or replace a section of a table.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu

NEW QUESTION # 212
You are implementing an Azure Stream Analytics solution to process event data from devices.
The devices output events when there is a fault and emit a repeat of the event every five seconds until the fault is resolved. The devices output a heartbeat event every five seconds after a previous event if there are no faults present.
A sample of the events is shown in the following table.

You need to calculate the uptime between the faults.
How should you complete the Stream Analytics SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: WHERE EventType='HeartBeat'
Box 2: ,TumblingWindow(Second, 5)
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.
The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/session-window-azure-stream-analytics
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

NEW QUESTION # 213
You need to design a data ingestion and storage solution for the Twitter feeds. The solution must meet the customer sentiment analytics requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection b worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control
Topic 1, Contoso
Transactional Date
Contoso has three years of customer, transactional, operation, sourcing, and supplier data comprised of 10 billion records stored across multiple on-premises Microsoft SQL Server servers. The SQL server instances contain data from various operational systems. The data is loaded into the instances by using SQL server integration Services (SSIS) packages.
You estimate that combining all product sales transactions into a company-wide sales transactions dataset will result in a single table that contains 5 billion rows, with one row per transaction.
Most queries targeting the sales transactions data will be used to identify which products were sold in retail stores and which products were sold online during different time period. Sales transaction data that is older than three years will be removed monthly.
You plan to create a retail store table that will contain the address of each retail store. The table will be approximately 2 MB. Queries for retail store sales will include the retail store addresses.
You plan to create a promotional table that will contain a promotion ID. The promotion ID will be associated to a specific product. The product will be identified by a product ID. The table will be approximately 5 GB.
Streaming Twitter Data
The ecommerce department at Contoso develops and Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes
Contoso plans to implement the following changes:
* Load the sales transaction dataset to Azure Synapse Analytics.
* Integrate on-premises data stores with Azure Synapse Analytics by using SSIS packages.
* Use Azure Synapse Analytics to analyze Twitter feeds to assess customer sentiments about products.
Sales Transaction Dataset Requirements
Contoso identifies the following requirements for the sales transaction dataset:
* Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong: to the partition on the right.
* Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
* Implement a surrogate key to account for changes to the retail store addresses.
* Ensure that data storage costs and performance are predictable.
* Minimize how long it takes to remove old records.
Customer Sentiment Analytics Requirement
Contoso identifies the following requirements for customer sentiment analytics:
* Allow Contoso users to use PolyBase in an A/ure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own A/ureAD credentials.
* Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
* Store Twitter feeds in Azure Storage by using Event Hubs Capture. The feeds will be converted into Parquet files.
* Ensure that the data store supports Azure AD-based access control down to the object level.
* Minimize administrative effort to maintain the Twitter feed data records.
* Purge Twitter feed data records;itftaitJ are older than two years.
Data Integration Requirements
Contoso identifies the following requirements for data integration:
Use an Azure service that leverages the existing SSIS packages to ingest on-premises data into datasets stored in a dedicated SQL pool of Azure Synaps Analytics and transform the data.
Identify a process to ensure that changes to the ingestion and transformation activities can be version controlled and developed independently by multiple data engineers.

NEW QUESTION # 214
You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

NEW QUESTION # 215
You have a Microsoft SQL Server database that uses a third normal form schema.
You plan to migrate the data in the database to a star schema in an A?ire Synapse Analytics dedicated SQI pool.
You need to design the dimension tables. The solution must optimize read operations.
What should you include in the solution? to answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 216
......

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Derek Rose Derek Rose

Biography

Free PDF DP-203 - Data Engineering on Microsoft Azure–High-quality Valid Test Fee

Valid Test DP-203 Bootcamp | DP-203 Latest Test Report

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q211-Q216):

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