DP-203 TEST PASS4SURE - FREE DP-203 TEST QUESTIONS

DP-203 Test Pass4sure - Free DP-203 Test Questions

DP-203 Test Pass4sure - Free DP-203 Test Questions

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Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is designed to test an individual's knowledge and skills in data engineering using Microsoft Azure. Data Engineering on Microsoft Azure certification is ideal for individuals who work with data and have experience with Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and Azure Stream Analytics. DP-203 Exam is intended to validate the candidate's ability to design, implement, and maintain data processing solutions using Microsoft's cloud platform.

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Free DP-203 Test Questions | Simulations DP-203 Pdf

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Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is an important certification for IT professionals who work with data solutions on Azure. It tests candidates on their knowledge and skills related to data engineering on Azure and can help them advance their careers and demonstrate their expertise to potential employers.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q316-Q321):

NEW QUESTION # 316
You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks. A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
A destination table in Azure Synapse
An Azure Blob storage container
A service principal
In which order should you perform the actions? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse


NEW QUESTION # 317
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Self-hosted integration runtime
A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.
Box 2: Schedule trigger
Schedule every 8 hours
Box 3: Copy activity
Scenario:
Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.


NEW QUESTION # 318
You have an Azure subscription that contains an Azure Synapse Analytics workspace named workspace1.
Workspace1 connects to an Azure DevOps repository named repo1. Repo1 contains a collaboration branch named main and a development branch named branch1. Branch1 contains an Azure Synapse pipeline named pipeline1.
In workspace1, you complete testing of pipeline1.
You need to schedule pipeline1 to run daily at 6 AM.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Explanation

Timeline Description automatically generated


NEW QUESTION # 319
You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytic dedicated SQL pool. The CSV file contains three columns named username, comment, and date.
The data flow already contains the following:
A source transformation.
A Derived Column transformation to set the appropriate types of data.
A sink transformation to land the data in the pool.
You need to ensure that the data flow meets the following requirements:
All valid rows must be written to the destination table.
Truncation errors in the comment column must be avoided proactively.
Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A. Add a select transformation to select only the rows that will cause truncation errors.
  • B. To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.
  • C. To the data flow, add a filter transformation to filter out rows that will cause truncation errors.
  • D. To the data flow, add a sink transformation to write the rows to a file in blob storage.

Answer: B,D

Explanation:
B: Example:
1. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.

2. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.
A:
3. Now we need to log the rows that failed. Add a sink transformation to the BadRows stream for logging. Here, we'll "auto-map" all of the fields so that we have logging of the complete transaction record. This is a text-delimited CSV file output to a single file in Blob Storage. We'll call the log file "badrows.csv".

4. The completed data flow is shown below. We are now able to split off error rows to avoid the SQL truncation errors and put those entries into a log file. Meanwhile, successful rows can continue to write to our target database.

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-data-flow-error-rows


NEW QUESTION # 320
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:

Explanation
Graphical user interface, text, application Description automatically generated

Box 1: Yes
A cluster mode of 'High Concurrency' is selected, unlike all the others which are 'Standard'. This results in a worker type of Standard_DS13_v2.
Box 2: No
When you run a job on a new cluster, the job is treated as a data engineering (job) workload subject to the job workload pricing. When you run a job on an existing cluster, the job is treated as a data analytics (all-purpose) workload subject to all-purpose workload pricing.
Box 3: Yes
Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns.
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 # 321
......

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