TRAINING MICROSOFT DP-203 FOR EXAM - DP-203 HIGH PASSING SCORE

Training Microsoft DP-203 For Exam - DP-203 High Passing Score

Training Microsoft DP-203 For Exam - DP-203 High Passing Score

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Tags: Training DP-203 For Exam, DP-203 High Passing Score, DP-203 Practice Exam Fee, DP-203 Valid Exam Bootcamp, Interactive DP-203 Practice Exam

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Microsoft DP-203 exam is a great way for professionals to enhance their skills and knowledge in data engineering on Azure. It is also a valuable certification for those who want to stand out in the job market and increase their chances of getting hired by top companies. With the growing demand for data engineers in today's digital age, obtaining this certification can open up new career opportunities for individuals.

Microsoft DP-203 (Data Engineering on Microsoft Azure) Exam is a certification that validates a professional's knowledge and skills in designing and implementing data solutions on Microsoft Azure. DP-203 Exam measures an individual's ability to perform tasks such as data storage, processing, and security using Azure technologies. To pass DP-203 exam, candidates must have a comprehensive understanding of Azure data services, including Azure Data Factory, Azure SQL Database, and Azure Cosmos DB.

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Microsoft DP-203 certification exam is designed to assess the skills and knowledge of data engineers who use Microsoft Azure to build and manage big data and analytics solutions. DP-203 exam validates the candidate's ability to design and implement data storage, data processing, and data integration solutions using Azure services such as Azure Data Lake Storage, Azure Databricks, Azure Stream Analytics, and Azure Data Factory. DP-203 Exam also tests the candidate's knowledge of data security, compliance, and governance in Azure.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q311-Q316):

NEW QUESTION # 311
You have an Azure event hub named retailhub that has 16 partitions. Transactions are posted to retailhub. Each transaction includes the transaction ID, the individual line items, and the payment details. The transaction ID is used as the partition key.
You are designing an Azure Stream Analytics job to identify potentially fraudulent transactions at a retail store. The job will use retailhub as the input. The job will output the transaction ID, the individual line items, the payment details, a fraud score, and a fraud indicator.
You plan to send the output to an Azure event hub named fraudhub.
You need to ensure that the fraud detection solution is highly scalable and processes transactions as quickly as possible.
How should you structure the output of the Stream Analytics job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#partitions


NEW QUESTION # 312
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.

All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 313
You have a SQL pool in Azure Synapse.
You discover that some queries fail or take a long time to complete.
You need to monitor for transactions that have rolled back.
Which dynamic management view should you query?

  • A. sys.dm_pdw_exec_sessions
  • B. sys.dm_pdw_waits
  • C. sys.dm_pdw_request_steps
  • D. sys.dm_pdw_nodes_tran_database_transactions

Answer: D

Explanation:
You can use Dynamic Management Views (DMVs) to monitor your workload including investigating query execution in SQL pool.
If your queries are failing or taking a long time to proceed, you can check and monitor if you have any transactions rolling back.
Example:
-- Monitor rollback
SELECT
SUM(CASE WHEN t.database_transaction_next_undo_lsn IS NOT NULL THEN 1 ELSE 0 END), t.pdw_node_id, nod.[type] FROM sys.dm_pdw_nodes_tran_database_transactions t JOIN sys.dm_pdw_nodes nod ON t.pdw_node_id = nod.pdw_node_id GROUP BY t.pdw_node_id, nod.[type] Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-manage-monitor#monitor-transaction-log-rollback


NEW QUESTION # 314
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of {YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

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


NEW QUESTION # 315
You have two Azure Storage accounts named Storage1 and Storage2. Each account holds one container and has the hierarchical namespace enabled. The system has files that contain data stored in the Apache Parquet format.
You need to copy folders and files from Storage1 to Storage2 by using a Data Factory copy activity. The solution must meet the following requirements:
No transformations must be performed.
The original folder structure must be retained.
Minimize time required to perform the copy activity.
How should you configure the copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application, chat or text message Description automatically generated

Box 1: Parquet
For Parquet datasets, the type property of the copy activity source must be set to ParquetSource.
Box 2: PreserveHierarchy
PreserveHierarchy (default): Preserves the file hierarchy in the target folder. The relative path of the source file to the source folder is identical to the relative path of the target file to the target folder.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/format-parquet
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage


NEW QUESTION # 316
......

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