DP-203 EXAM SUCCESS & DP-203 PRACTICE TESTS

DP-203 Exam Success & DP-203 Practice Tests

DP-203 Exam Success & DP-203 Practice Tests

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Tags: DP-203 Exam Success, DP-203 Practice Tests, DP-203 Top Questions, Free DP-203 Vce Dumps, DP-203 Test Questions Answers

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Microsoft DP-203 exam is designed for data engineers who want to demonstrate their expertise in designing and implementing data solutions on Microsoft Azure. DP-203 exam focuses on the deployment of various data platform technologies such as Azure Stream Analytics, Azure Data Factory, Azure Cosmos DB, and Azure SQL Database. The DP-203 exam is an associate-level certification that validates a candidate's skills and knowledge required to design, implement and maintain data solutions on Azure.

To pass the DP-203 Exam, candidates must demonstrate their ability to design and implement data solutions on Azure by answering a series of multiple-choice questions and scenario-based questions. DP-203 exam is timed and lasts 150 minutes, and candidates must score at least 700 out of 1000 to pass.

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DP-203 Practice Tests | DP-203 Top Questions

Passing the Data Engineering on Microsoft Azure DP-203 exam is your best career opportunity. The rich experience with relevant certificates is important for enterprises to open up a series of professional vacancies for your choices. Our Microsoft DP-203 learning quiz bank and learning materials look up the latest DP-203 questions and answers based on the topics you choose.

Microsoft DP-203: Data Engineering on Microsoft Azure exam is a great way for professionals to demonstrate their expertise in data engineering on Azure and stand out in the competitive job market. With the right preparation and training, candidates can pass the exam and obtain a valuable certification that can help accelerate their career growth.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q212-Q217):

NEW QUESTION # 212
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 # 213
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

Answer: B

Explanation:
If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not an Azure Databricks notebook, with your own data processing logic and use the activity in the pipeline.
You can create a custom activity to run R scripts on your HDInsight cluster with R installed.
Reference:
https://docs.microsoft.com/en-US/azure/data-factory/transform-data


NEW QUESTION # 214
You need to implement an Azure Synapse Analytics database object for storing the sales transactions dat a. The solution must meet the sales transaction dataset requirements.
What solution must meet the sales transaction dataset requirements.
What should you do? 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/sql/t-sql/statements/create-table-azure-sql-data-warehouse


NEW QUESTION # 215
You are designing an application that will use an Azure Data Lake Storage Gen 2 account to store petabytes of license plate photos from toll booths. The account will use zone-redundant storage (ZRS).
You identify the following usage patterns:
* The data will be accessed several times a day during the first 30 days after the data is created. The data must meet an availability SU of 99.9%.
* After 90 days, the data will be accessed infrequently but must be available within 30 seconds.
* After 365 days, the data will be accessed infrequently but must be available within five minutes.

Answer:

Explanation:

Explanation
Box 1: Hot
The data will be accessed several times a day during the first 30 days after the data is created. The data must meet an availability SLA of 99.9%.
Box 2: Cool
After 90 days, the data will be accessed infrequently but must be available within 30 seconds.
Data in the Cool tier should be stored for a minimum of 30 days.
When your data is stored in an online access tier (either Hot or Cool), users can access it immediately. The Hot tier is the best choice for data that is in active use, while the Cool tier is ideal for data that is accessed less frequently, but that still must be available for reading and writing.
Box 3: Cool
After 365 days, the data will be accessed infrequently but must be available within five minutes.
Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview
https://docs.microsoft.com/en-us/azure/storage/blobs/archive-rehydrate-overview


NEW QUESTION # 216
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:

Explanation:
Table Description automatically generated

Box 1: Incremental load
Box 2: Tumbling window
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Timeline Description automatically generated

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


NEW QUESTION # 217
......

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