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Microsoft DP-203 Exam Dumps

Exam Name: Data Engineering on Microsoft Azure
Certification Name: Microsoft Azure Data Engineer Associate

Microsoft DP-203 exam dumps are created by industry top professionals and after that its also verified by expert team. We are providing you updated Data Engineering on Microsoft Azure exam questions answers. We keep updating our Microsoft Azure Data Engineer Associate practice test according to real exam. So prepare from our latest questions answers and pass your exam.

  • Total Questions: 289
  • Last Updation Date: 2-Jun-2023

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You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container.
Which resource provider should you enable?

 

Microsoft.Sql

 

Microsoft-Automation

 

Microsoft.EventGrid

 

Microsoft.EventHub


Microsoft.EventGrid


Explanation:
Event-driven architecture (EDA) is a common data integration pattern that involves
production, detection, consumption, and reaction to events. Data integration scenarios
often require Data Factory customers to trigger pipelines based on events happening in
storage account, such as the arrival or deletion of a file in Azure Blob Storage account.
Data Factory natively integrates with Azure Event Grid, which lets you trigger pipelines on
such events.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-event-trigger
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers




You have an Azure Stream Analytics query. The query returns a result set that contains
10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

 

Add a pass-through query.

 

Add a temporal analytic function.

 

Scale out the query by using PARTITION BY.

 

Convert the query to a reference query.

 

Increase the number of streaming units.


Scale out the query by using PARTITION BY.


Increase the number of streaming units.





You have an Azure data solution that contains an enterprise data warehouse in Azure Synapse Analytics named DW1.
Several users execute ad hoc queries to DW1 concurrently.
You regularly perform automated data loads to DW1.
You need to ensure that the automated data loads have enough memory available to complete quickly and successfully when the adhoc queries run.
What should you do?

 

Hash distribute the large fact tables in DW1 before performing the automated data loads.

 

Assign a smaller resource class to the automated data load queries.

 

Assign a larger resource class to the automated data load queries.

 

Create sampled statistics for every column in each table of DW1.


Assign a larger resource class to the automated data load queries.


Explanation:
The performance capacity of a query is determined by the user's resource class. Resource
classes are pre-determined resource limits in Synapse SQL pool that govern compute
resources and concurrency for query execution.
Resource classes can help you configure resources for your queries by setting limits on the
number of queries that run concurrently and on the compute-resources assigned to each
query. There's a trade-off between memory and concurrency.
Smaller resource classes reduce the maximum memory per query, but increase
concurrency.
Larger resource classes increase the maximum memory per query, but reduce
concurrency.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/resourceclasses-
for-workload-management


Microsoft DP-203 dumps

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