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Taking the Microsoft DP-203 exam can lead to numerous benefits for data engineers. It can improve their career prospects by validating their skills and knowledge in data engineering on the Azure platform. It can also provide them with a competitive edge over other data engineering professionals by demonstrating their expertise in using Azure data services. Additionally, passing the exam can lead to recognition and respect from peers and employers.

The DP-203 exam consists of multiple choice questions that cover a range of topics, including data storage, data processing, data security, and data monitoring. DP-203 exam is timed and lasts for about 150 minutes. Microsoft recommends that candidates have at least two years of experience working with Azure data services before taking the DP-203 exam. Data Engineering on Microsoft Azure certification is valid for two years, after which candidates must retake the exam or earn a different Azure certification to maintain their credentials.



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The Data Engineering on Microsoft Azure DP-203 certification provides both novices and experts with a fantastic opportunity to show off their knowledge of and proficiency in carrying out a particular task. With the Microsoft DP-203 exam, you will have the chance to update your knowledge while obtaining dependable evidence of your proficiency. You can also get help from actual Data Engineering on Microsoft Azure DP-203 Exam Questions and pass your dream Data Engineering on Microsoft Azure DP-203 certification exam.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q292-Q297):

NEW QUESTION # 292
The following code segment is used to create an Azure Databricks cluster.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


Answer:

Explanation:

Reference:
https://adatis.co.uk/databricks-cluster-sizing/
https://docs.microsoft.com/en-us/azure/databricks/jobs
https://docs.databricks.com/administration-guide/capacity-planning/cmbp.html
https://docs.databricks.com/delta/index.html

NEW QUESTION # 293
You have the following Azure Stream Analytics query.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


Answer:

Explanation:

Explanation:

Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
10 partitions x six SUs = 60 SUs is fine.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/

NEW QUESTION # 294
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
* Serve as a repository (or high volumes of large files in various formats.
* Implement optimized storage for big data analytics workloads.
* Ensure that data can be organized using a hierarchical structure.
Batch processing:
* Use a managed solution for in-memory computation processing.
* Natively support Scala, Python, and R programming languages.
* Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? 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-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION # 295
You are designing an application that will store petabytes of medical imaging data When the data is first created, the data will be accessed frequently during the first week. After one month, the data must be accessible within 30 seconds, but files will be accessed infrequently. After one year, the data will be accessed infrequently but must be accessible within five minutes.
You need to select a storage strategy for the data. The solution must minimize costs.
Which storage tier should you use for each time frame? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Answer:

Explanation:

Explanation

First week: Hot
Hot - Optimized for storing data that is accessed frequently.
After one month: Cool
Cool - Optimized for storing data that is infrequently accessed and stored for at least 30 days.
After one year: Cool

NEW QUESTION # 296
You are creating a new notebook in Azure Databricks that will support R as the primary language but will also support Scale and SOL Which switch should you use to switch between languages?

* A. %<Language>
* B. \\(<Language>)
* C. \\(<Language>)
* D. @<Language>
Answer: A

Explanation:
To change the language in Databricks' cells to either Scala, SQL, Python or R, prefix the cell with '%', followed by the language.
%python //or r, scala, sql
Reference:
https://www.theta.co.nz/news-blogs/tech-blog/enhancing-digital-twins-part-3-predictive-maintenance-with-azure

NEW QUESTION # 297
......

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