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Microsoft DP-203 Exam is designed to test the candidate's knowledge of data engineering concepts and practices on Azure. It evaluates the candidate's ability to design and implement data storage solutions, data processing solutions, and data security solutions on Azure. It also assesses the candidate's knowledge of monitoring, troubleshooting, and optimizing data solutions on Azure.

The Microsoft DP-203 exam is ideal for professionals who have experience working with Azure data services and are looking to enhance their skills and knowledge in the field of data engineering. Data Engineering on Microsoft Azure certification is also ideal for those who are looking to advance their career in the field of data engineering and want to demonstrate their expertise in designing and implementing data solutions on Microsoft Azure.

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Microsoft DP-203 (Data Engineering on Microsoft Azure) certification is a highly sought-after accreditation for data engineers who want to demonstrate their knowledge and skills in designing and implementing data solutions on the Azure platform. Data Engineering on Microsoft Azure certification validates a candidate's technical expertise in building data processing systems, data storage solutions, and data transformation and integration solutions using Azure services.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q158-Q163):

NEW QUESTION # 158
You use PySpark in Azure Databricks to parse the following JSON input.

You need to output the data in the following tabular format.

How should you complete the PySpark code? To answer, drag the appropriate values to he correct targets.
Each value may be used once, more than once or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application Description automatically generated

Box 1: select
Box 2: explode
Bop 3: alias
pyspark.sql.Column.alias returns this column aliased with a new name or names (in the case of expressions that return more than one column, such as explode).
Reference:
https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.Column.alias.html
https://docs.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/explode

NEW QUESTION # 159
You have an Azure event hub named retailhub that has 16 partitions. Transactions are posted to retailhub.
Each transaction includes the transaction ID, the individual line items, and the payment details. The transaction ID is used as the partition key.
You are designing an Azure Stream Analytics job to identify potentially fraudulent transactions at a retail store. The job will use retailhub as the input. The job will output the transaction ID, the individual line items, the payment details, a fraud score, and a fraud indicator.
You plan to send the output to an Azure event hub named fraudhub.
You need to ensure that the fraud detection solution is highly scalable and processes transactions as quickly as possible.
How should you structure the output of the Stream Analytics job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: 16
For Event Hubs you need to set the partition key explicitly.
An embarrassingly parallel job is the most scalable scenario in Azure Stream Analytics. It connects one partition of the input to one instance of the query to one partition of the output.
Box 2: Transaction ID
Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#partitions

NEW QUESTION # 160
You are designing an Azure Data Lake Storage Gen2 structure for telemetry data from 25 million devices distributed across seven key geographical regions. Each minute, the devices will send a JSON payload of metrics to Azure Event Hubs.
You need to recommend a folder structure for the data. The solution must meet the following requirements:
* Data engineers from each region must be able to build their own pipelines for the data of their respective region only.
* The data must be processed at least once every 15 minutes for inclusion in Azure Synapse Analytics serverless SQL pools.
How should you recommend completing the structure? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Box 1: {YYYY}/{MM}/{DD}/{HH}
Date Format [optional]: if the date token is used in the prefix path, you can select the date format in which your files are organized. Example: YYYY/MM/DD Time Format [optional]: if the time token is used in the prefix path, specify the time format in which your files are organized. Currently the only supported value is HH.
Box 2: {regionID}/raw
Data engineers from each region must be able to build their own pipelines for the data of their respective region only.
Box 3: {deviceID}
Reference:
https://github.com/paolosalvatori/StreamAnalyticsAzureDataLakeStore/blob/master/README.md

NEW QUESTION # 161
You have data stored in thousands of CSV files in Azure Data Lake Storage Gen2. Each file has a header row followed by a properly formatted carriage return (/r) and line feed (/n).
You are implementing a pattern that batch loads the files daily into an enterprise data warehouse in Azure Synapse Analytics by using PolyBase.
You need to skip the header row when you import the files into the data warehouse. Before building the loading pattern, you need to prepare the required database objects in Azure Synapse Analytics.
Which three 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: Each correct selection is worth one point

Answer:

Explanation:

Step 1: Create an external data source that uses the abfs location
Create External Data Source to reference Azure Data Lake Store Gen 1 or 2 Step 2: Create an external file format and set the First_Row option.
Create External File Format.
Step 3: Use CREATE EXTERNAL TABLE AS SELECT (CETAS) and configure the reject options to specify reject values or percentages To use PolyBase, you must create external tables to reference your external data.
Use reject options.
Note: REJECT options don't apply at the time this CREATE EXTERNAL TABLE AS SELECT statement is run. Instead, they're specified here so that the database can use them at a later time when it imports data from the external table. Later, when the CREATE TABLE AS SELECT statement selects data from the external table, the database will use the reject options to determine the number or percentage of rows that can fail to import before it stops the import.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/polybase/polybase-t-sql-objects
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-external-table-as-select-transact-sql

NEW QUESTION # 162
You have two Azure SQL databases named DB1 and DB2.
DB1 contains a table named Table 1. Table1 contains a timestamp column named LastModifiedOn.
LastModifiedOn contains the timestamp of the most recent update for each individual row.
DB2 contains a table named Watermark. Watermark contains a single timestamp column named WatermarkValue.
You plan to create an Azure Data Factory pipeline that will incrementally upload into Azure Blob Storage all the rows in Table1 for which the LastModifiedOn column contains a timestamp newer than the most recent value of the WatermarkValue column in Watermark.
You need to identify which activities to include in the pipeline. The solution must meet the following requirements:
* Minimize the effort to author the pipeline.
* Ensure that the number of data integration units allocated to the upload operation can be controlled.
What should you identify? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 163
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