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The Microsoft DP-203 exam tests the candidates' knowledge of data engineering principles, data storage options on Azure, data processing using Azure services, data transformation using Azure Databricks, and data integration using Azure Data Factory. The exam also covers topics such as data ingestion using Azure Stream Analytics, data orchestration using Azure Synapse Analytics, and data security and compliance.

To take the DP-203 exam, candidates need to have a good understanding of data engineering concepts and experience working with Azure services. It is recommended that candidates have experience with data storage solutions, data processing, and data security. Candidates can prepare for the exam by taking the official Microsoft training course, DP-203T00-A: Data Engineering on Microsoft Azure, or by using study materials available online.

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NEW QUESTION # 280
You have the following Azure Data Factory pipelines
* ingest Data from System 1
* Ingest Data from System2
* Populate Dimensions
* Populate facts
ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours.
What should you do to schedule the pipelines for execution?

Answer: A

NEW QUESTION # 281
You are batch loading a table in an Azure Synapse Analytics dedicated SQL pool.
You need to load data from a staging table to the target table. The solution must ensure that if an error occurs while loading the data to the target table, all the inserts in that batch are undone.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 282
You use Azure Data Factory to prepare data to be queried by Azure Synapse Analytics serverless SQL pools.
Files are initially ingested into an Azure Data Lake Storage Gen2 account as 10 small JSON files. Each file contains the same data attributes and data from a subsidiary of your company.
You need to move the files to a different folder and transform the data to meet the following requirements:
Provide the fastest possible query times.
Automatically infer the schema from the underlying files.
How should you configure the Data Factory copy activity? 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/storage/blobs/data-lake-storage-introduction
https://docs.microsoft.com/en-us/azure/data-factory/format-parquet

NEW QUESTION # 283
You have a table in an Azure Synapse Analytics dedicated SQL pool. The table was created by using the following Transact-SQL statement.

You need to alter the table to meet the following requirements:
* Ensure that users can identify the current manager of employees.
* Support creating an employee reporting hierarchy for your entire company.
* Provide fast lookup of the managers' attributes such as name and job title.
Which column should you add to the table?

Answer: D

Explanation:
Use the same definition as the EmployeeID column.
Reference:
https://docs.microsoft.com/en-us/analysis-services/tabular-models/hierarchies-ssas-tabular

NEW QUESTION # 284
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model. Pool1 will contain the following tables.

You need to design the table storage for pool1. The solution must meet the following requirements:
* Maximize the performance of data loading operations to Staging.WebSessions.
* Minimize query times for reporting queries against the dimensional model.
Which type of table distribution should you use for each table? To answer, drag the appropriate table distribution types to the correct tables. Each table distribution type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Box 1: Replicated
The best table storage option for a small table is to replicate it across all the Compute nodes.
Box 2: Hash
Hash-distribution improves query performance on large fact tables.
Box 3: Round-robin
Round-robin distribution is useful for improving loading speed.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu

NEW QUESTION # 285
......

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