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Question # 1



Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are designing an Azure Stream Analytics solution that will analyze Twitter data.

You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.

Solution: You use a tumbling window, and you set the window size to 10 seconds.

Does this meet the goal?
A. Yes
B. No



A.
  Yes




Explanation:

A tumbling window is the correct type of window for counting events (such as tweets) in distinct, non-overlapping time intervals, ensuring that each event (tweet) is counted only once in each time window. Since the window size is set to 10 seconds, it will count all tweets in each 10-second interval without overlap, achieving the desired outcome. Thus, the solution of using a tumbling window with a 10-second window size does meet the goal.




Question # 2



You are designing the folder structure for an Azure Data Lake Storage Gen2 account.
You identify the following usage patterns:
• Users will query data by using Azure Synapse Analytics serverless SQL pools and Azure
Synapse Analytics serverless Apache Spark pods.
• Most queries will include a filter on the current year or week.
• Data will be secured by data source.
You need to recommend a folder structure that meets the following requirements:
• Supports the usage patterns
• Simplifies folder security
• Minimizes query times
Which folder structure should you recommend?

A.

Option A

B.

Option B

C.

Option C

D.

Option D

E.

Option E




D.
  

Option D







Question # 3



You have an Azure Databricks resource.
You need to log actions that relate to changes in compute for the Databricks resource.
Which Databricks services should you log?

A.

clusters

B.

workspace

C.

DBFS

D.

SSH

E.

lobs




B.
  

workspace







Question # 4



You have an Azure Data lake Storage account that contains a staging zone.
You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a
data warehouse in Azure Synapse Analytics.
Solution You use an Azure Data Factory schedule trigger to execute a pipeline that
executes an Azure Databricks notebook, and then inserts the data into the data warehouse
Dow this meet the goal?

A.

Yes

B.

No




A.
  

Yes







Question # 5



You need to implement a Type 3 slowly changing dimension (SCD) for product category
data in an Azure Synapse Analytics dedicated SQL pool.
You have a table that was created by using the following Transact-SQL statement.

Which two columns should you add to the table? Each correct answer presents part of the
solution.
NOTE: Each correct selection is worth one point.

A.

[EffectiveScarcDate] [datetime] NOT NULL,

B.

[CurrentProduccCacegory] [nvarchar] (100) NOT NULL,

C.

[EffectiveEndDace] [dacecime] NULL,

D.

[ProductCategory] [nvarchar] (100) NOT NULL,

E.

[OriginalProduccCacegory] [nvarchar] (100) NOT NULL,




B.
  

[CurrentProduccCacegory] [nvarchar] (100) NOT NULL,




E.
  

[OriginalProduccCacegory] [nvarchar] (100) NOT NULL,



Explanation:
A Type 3 SCD supports storing two versions of a dimension member as separate columns.
The table includes a column for the current value of a member plus either the original or
previous value of the member. So Type 3 uses additional columns to track one key
instance of history, rather than storing additional rows to track each change like in a Type 2 





Question # 6



You plan to build a structured streaming solution in Azure Databricks. The solution will count new events in five-minute intervals and report only events that arrive during the interval. The output will be sent to a Delta Lake table.
Which output mode should you use?

A.

complete

B.

update

C.

append




C.
  

append



Explanation: Append Mode: Only new rows appended in the result table since the last
trigger are written to external storage. This is applicable only for the queries where existing
rows in the Result Table are not expected to change.
https://docs.databricks.com/getting-started/spark/streaming.html





Question # 7



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?

A.

Microsoft.Sql

B.

Microsoft-Automation

C.

Microsoft.EventGrid

D.

Microsoft.EventHub




C.
  

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





Question # 8



You are designing a financial transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
TransactionType: 40 million rows per transaction type
CustomerSegment: 4 million per customer segment
TransactionMonth: 65 million rows per month
AccountType: 500 million per account type
You have the following query requirements:
Analysts will most commonly analyze transactions for a given month.
Transactions analysis will typically summarize transactions by transaction type,
customer segment, and/or account type
You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?

A.

CustomerSegment

B.

AccountType

C.

TransactionType

D.

TransactionMonth




D.
  

TransactionMonth



Explanation:
For optimal compression and performance of clustered columnstore tables, a minimum of 1
million rows per distribution and partition is needed. Before partitions are created,
dedicated SQL pool already divides each table into 60 distributed databases.
Example: Any partitioning added to a table is in addition to the distributions created behind
the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and
given that a dedicated SQL pool has 60 distributions, then the sales fact table should
contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a
table contains fewer than the recommended minimum number of rows per partition,
consider using fewer partitions in order to increase the number of rows per partition.





Question # 9



You have an Azure Stream Analytics job.
You need to ensure that the job has enough streaming units provisioned
You configure monitoring of the SU % Utilization metric.
Which two additional metrics should you monitor? Each correct answer presents part of the
solution.
NOTE Each correct selection is worth one point

A.

Out of order Events

B.

Late Input Events

C.

Baddogged Input Events

D.

Function Events




C.
  

Baddogged Input Events







Question # 10



You plan to perform batch processing in Azure Databricks once daily.
Which type of Databricks cluster should you use?

A.

High Concurrency

B.

automated

C.

interactive




B.
  

automated



Explanation:
Azure Databricks has two types of clusters: interactive and automated. You use interactive
clusters to analyze data collaboratively with interactive notebooks. You use automated
clusters to run fast and robust automated jobs.
Example: Scheduled batch workloads (data engineers running ETL jobs)
This scenario involves running batch job JARs and notebooks on a regular cadence
through the Databricks platform.
The suggested best practice is to launch a new cluster for each run of critical jobs. This
helps avoid any issues (failures, missing SLA, and so on) due to an existing workload
(noisy neighbor) on a shared cluster.
Reference:
https://docs.databricks.com/administration-guide/cloudconfigurations/
aws/cmbp.html#scenario-3-scheduled-batch-workloads-data-engineersrunning-
etl-jobs




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Data Engineering on Microsoft Azure Exam Dumps


Exam Code: DP-203
Exam Name: Data Engineering on Microsoft Azure

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