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Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is a highly sought-after certification for professionals in the data engineering field. DP-203 exam is designed to test the candidate's knowledge and skills in implementing data solutions using Microsoft Azure technologies. DP-203 Exam covers a wide range of topics, including data storage, data processing, data analysis, and data visualization. Candidates who pass DP-203 exam are recognized as experts in designing, implementing, and maintaining data solutions on the Azure platform.

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Microsoft DP-203: Data Engineering on Microsoft Azure is a certification exam that validates the skills and knowledge of professionals in data engineering on Azure. DP-203 Exam covers various topics such as data storage, data processing, data transformation, and data integration using Azure services. DP-203 exam is suitable for data engineers who work with Azure services such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics. Data Engineering on Microsoft Azure certification is globally recognized and is highly valued by employers, making it an excellent choice for professionals who want to advance their careers in data engineering.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q192-Q197):

NEW QUESTION # 192
You have an Azure subscription that contains an Azure Data Lake Storage Gen2 account named storage1 and an Azure Synapse Analytics workspace named Workspace1. Workspace1 has a serverless SQL pool.
You use the serverless SQL pool to query customer orders from the files in storage1.
You run the following query.
SELECT *
FROM OPENROWSET(BULK 'https://storage1.blob.core.windows.net/data/orders/year =* /month =* / *.* ', FORMAT = 'parquet') AS customerorders WHERE customerorders. filepath(1) = '2024' AND customerorders.filepath(2) IN ('3','4'); 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:
Storage1 provides a hierarchical namespace: Yes
Files from March 2025 will be included: No
Only files that have a Parquet file extension will be included: Yes
Query Breakdown
* Data Source:
* The OPENROWSET function queries data stored in Azure Data Lake Storage Gen2 (storage1) using the serverless SQL pool in Synapse Analytics.
* The data is stored in Parquet files in the folder structure data/orders/year=YYYY/month=MM/.
* Query Filter:
* The filter conditions in the query are:
* customerorders.filepath(1) = '2024': Limits the query to files in the folder year=2024.
* customerorders.filepath(2) IN ('3', '4'): Limits the query to files in the subfolders month=3 or month=4.
* File Format:
* The FORMAT = 'parquet' clause specifies that only Parquet files will be queried.
Statements Analysis
* Storage1 provides a hierarchical namespace.aswer: Yes
* Azure Data Lake Storage Gen2 supports a hierarchical namespace, which enables folder-based organization.
* The folder structure (e.g., data/orders/year=2024/month=3/) demonstrates the use of a hierarchical namespace.
* Files from March 2025 will be included.aswer: No
* The query explicitly filters for year=2024, so files from 2025 will not be included in the results.
* Only files that have a Parquet file extension will be included.aswer: Yes
* The FORMAT = 'parquet' clause in the query ensures that only Parquet files are queried. Files with other extensions (e.g., .csv or .json) will not be included.

NEW QUESTION # 193
You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.
You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.
You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Azure Stream Analytics
Box 2: Hopping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.
Box 3: Point within polygon
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

NEW QUESTION # 194
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:

Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption

NEW QUESTION # 195
You have an Azure subscription that contains an Azure Databricks workspace named databricks1 and an Azure Synapse Analytics workspace named synapse1. The synapse1 workspace contains an Apache Spark pool named pool1.
You need to share an Apache Hive catalog of pool1 with databricks1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Azure SQL Database
Use external Hive Metastore for Synapse Spark Pool
Azure Synapse Analytics allows Apache Spark pools in the same workspace to share a managed HMS (Hive Metastore) compatible metastore as their catalog.
Set up linked service to Hive Metastore
Follow below steps to set up a linked service to the external Hive Metastore in Synapse workspace.
Open Synapse Studio, go to Manage > Linked services at left, click New to create a new linked service.
Set up Hive Metastore linked service
Choose Azure SQL Database or Azure Database for MySQL based on your database type, click Continue.
Provide Name of the linked service. Record the name of the linked service, this info will be used to configure Spark shortly.
You can either select Azure SQL Database/Azure Database for MySQL for the external Hive Metastore from Azure subscription list, or enter the info manually.
Provide User name and Password to set up the connection.
Test connection to verify the username and password.
Click Create to create the linked service.
Box 2: A Hive Metastore
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-external-metastore

NEW QUESTION # 196
You need to design the partitions for the product sales transactions. The solution must mee the sales transaction dataset requirements.
What should you include in 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/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION # 197
......

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