[Dec-2025] Latest Microsoft DP-203 Certification Practice Test Questions
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Microsoft DP-203 certification exam is intended for data engineers who work with data scientists, data analysts, and business stakeholders to implement data solutions on the Microsoft Azure platform. DP-203 exam covers various topics such as Azure data storage, Azure data processing, Azure data integration, and data transformation. It also tests your knowledge of key Azure services such as Azure Data Factory, Azure Synapse Analytics, and Azure Stream Analytics.
To prepare for the DP-203 exam, candidates can leverage a variety of resources offered by Microsoft, such as official study guides, online training courses, and practice tests. Microsoft also recommends that candidates have hands-on experience with Azure-based data solutions before taking the exam. This can be achieved through practical work experience or by using Azure's free trial environment to gain hands-on experience with Azure-based data services.
Microsoft DP-203 certification exam is an excellent opportunity for data engineers and other IT professionals to validate their skills and expertise in designing, implementing, and managing data solutions on the Azure platform. Successful completion of DP-203 exam demonstrates a candidate's proficiency in Azure-based data solutions and can lead to increased career prospects and earning potential. With the right preparation and practical experience, candidates can confidently take the DP-203 exam and earn a globally recognized certification from Microsoft.
NEW QUESTION # 69
You need to collect application metrics, streaming query events, and application log messages for an Azure Databrick cluster.
Which type of library and workspace should you implement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
You can send application logs and metrics from Azure Databricks to a Log Analytics workspace. It uses the Azure Databricks Monitoring Library, which is available on GitHub.
References:
https://docs.microsoft.com/en-us/azure/architecture/databricks-monitoring/application-logs
NEW QUESTION # 70
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 # 71
You have an Azure subscription that contains an Azure Data Lake Storage account. The storage account contains a data lake named DataLake1.
You plan to use an Azure data factory to ingest data from a folder in DataLake1, transform the data, and land the data in another folder.
You need to ensure that the data factory can read and write data from any folder in the DataLake1 file system.
The solution must meet the following requirements:
* Minimize the risk of unauthorized user access.
* Use the principle of least privilege.
* Minimize maintenance effort.
How should you configure access to the storage account for the data factory? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Text Description automatically generated with low confidence
Box 1: Azure Active Directory (Azure AD)
On Azure, managed identities eliminate the need for developers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens.
Box 2: a managed identity
A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Data Lake Storage Gen2 authentication, similar to using your own service principal. It allows this designated factory to access and copy data to or from your Data Lake Storage Gen2.
Note: The Azure Data Lake Storage Gen2 connector supports the following authentication types.
* Account key authentication
* Service principal authentication
* Managed identities for Azure resources authentication
Reference:
https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/overview
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage
NEW QUESTION # 72
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:
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 # 73
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 # 74
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model. Pool1 will contain the following tables.
You need to design the table storage for pool1. The solution must meet the following requirements:
Maximize the performance of data loading operations to Staging.WebSessions.
Minimize query times for reporting queries against the dimensional model.
Which type of table distribution should you use for each table? To answer, drag the appropriate table distribution types to the correct tables. Each table distribution type 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:
NEW QUESTION # 75
You have a table named SalesFact in an enterprise data warehouse in Azure Synapse Analytics. SalesFact contains sales data from the past 36 months and has the following characteristics:
* Is partitioned by month
* Contains one billion rows
* Has clustered columnstore indexes
At the beginning of each month, you need to remove data from SalesFact that is older than 36 months as quickly as possible.
Which three actions should you perform in sequence in a stored procedure? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Step 1: Create an empty table named SalesFact_work that has the same schema as SalesFact.
Step 2: Switch the partition containing the stale data from SalesFact to SalesFact_Work.
SQL Data Warehouse supports partition splitting, merging, and switching. To switch partitions between two tables, you must ensure that the partitions align on their respective boundaries and that the table definitions match.
Loading data into partitions with partition switching is a convenient way stage new data in a table that is not visible to users the switch in the new data.
Step 3: Drop the SalesFact_Work table.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition
NEW QUESTION # 76
You have a Microsoft Entra tenant.
The tenant contains an Azure Data Lake Storage Gen2 account named storage! that has two containers named fs1 and fs2. You have a Microsoft Entra group named OepartmentA. You need to meet the following requirements:
* OepartmentA must be able to read, write, and list all the files in fs1.
* OepartmentA must be prevented from accessing any files in fs2
* The solution must use the principle of least privilege.
Which role should you assign to DepartmentA?
- A. Storage Blob Data Contributor for storage1
- B. Storage Blob Data Owner for fsl
- C. Storage Blob Data Contributor for fsl
- D. Contributor for fsl
Answer: C
NEW QUESTION # 77
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains an external table named Sales. Sales contains sales dat a. Each row in Sales contains data on a single sale, including the name of the salesperson.
You need to implement row-level security (RLS). The solution must ensure that the salespeople can access only their respective sales.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 78
You are building an Azure Stream Analytics job to identify how much time a user spends interacting with a feature on a webpage.
The job receives events based on user actions on the webpage. Each row of data represents an event. Each event has a type of either 'start' or 'end'.
You need to calculate the duration between start and end events.
How should you complete the query? 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/stream-analytics/stream-analytics-stream-analytics-query-patterns
NEW QUESTION # 79
You have a table named SalesFact in an enterprise data warehouse in Azure Synapse Analytics. SalesFact contains sales data from the past 36 months and has the following characteristics:
Is partitioned by month
Contains one billion rows
Has clustered columnstore indexes
At the beginning of each month, you need to remove data from SalesFact that is older than 36 months as quickly as possible.
Which three actions should you perform in sequence in a stored procedure? 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/sql-data-warehouse/sql-data-warehouse-tables-partition
NEW QUESTION # 80
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
New data is accessed frequently and must be available as quickly as possible.
Data that is older than five years is accessed infrequently but must be available within one second when requested.
Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
Costs must be minimized while maintaining the required availability.
How should you manage the data? 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/storage-blob-storage-tiers
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-the-general-availability-of-replicated-tables/
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/
NEW QUESTION # 81
You have an Azure Synapse Analytics dedicated SQL pool named pool1.
You plan to implement a star schema in pool1 and create a new table named DimCustomer by using the following code.
You need to ensure that DimCustomer has the necessary columns to support a Type 2 slowly changing dimension (SCD). Which two columns should you add? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. [EffectiveStartDate] [datetime] NOT NULL
- B. [EffectiveEndDate] [datetime] NOT NULL
- C. [HistoricalSalesPerson] [nvarchar] (256) NOT NULL
- D. [RowID] [bigint] NOT NULL
- E. [PreviousModifiedDate] [datetime] NOT NULL
Answer: B,C
NEW QUESTION # 82
You are creating an Apache Spark job in Azure Databricks that will ingest JSON-formatted data.
You need to convert a nested JSON string into a DataFrame that will contain multiple rows.
Which Spark SQL function should you use?
- A. extract
- B. explode
- C. filter
- D. coalesce
Answer: B
Explanation:
Explanation
Convert nested JSON to a flattened DataFrame
You can to flatten nested JSON, using only $"column.*" and explode methods.
Note: Extract and flatten
Use $"column.*" and explode methods to flatten the struct and array types before displaying the flattened DataFrame.
Scala
display(DF.select($"id" as "main_id",$"name",$"batters",$"ppu",explode($"topping")) // Exploding the topping column using explode as it is an array type withColumn("topping_id",$"col.id") // Extracting topping_id from col using DOT form withColumn("topping_type",$"col.type") // Extracting topping_tytpe from col using DOT form drop($"col") select($"*",$"batters.*") // Flattened the struct type batters tto array type which is batter drop($"batters") select($"*",explode($"batter")) drop($"batter") withColumn("batter_id",$"col.id") // Extracting batter_id from col using DOT form withColumn("battter_type",$"col.type") // Extracting battter_type from col using DOT form drop($"col") ) Reference: https://learn.microsoft.com/en-us/azure/databricks/kb/scala/flatten-nested-columns-dynamically
NEW QUESTION # 83
From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays.
The data contains the following columns.
You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension.
To which table should you add each column? 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: DimEvent
Box 2: DimChannel
Box 3: FactEvents
Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc Reference:
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema
NEW QUESTION # 84
You have an Azure subscription that contains an Azure Databricks workspace. The workspace contains a notebook named Notebook1. In Notebook1, you create an Apache Spark DataFrame named df_sales that contains the following columns:
* Customer
* Salesperson
* Region
* Amount
You need to identify the three top performing salespersons by amount for a region named HQ.
How should you complete the query? 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.
Answer:
Explanation:
NEW QUESTION # 85
You have an Azure subscription that contains an Azure SQL database named DB1 and a storage account named storage1. The storage1 account contains a file named File1.txt. File1.txt contains the names of selected tables in DB1.
You need to use an Azure Synapse pipeline to copy data from the selected tables in DB1 to the files in storage1. The solution must meet the following requirements:
* The Copy activity in the pipeline must be parameterized to use the data in File1.txt to identify the source and destination of the copy.
* Copy activities must occur in parallel as often as possible.
Which two pipeline activities should you include in the pipeline? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. Get Metadata
- B. ForEach
- C. Lookup
- D. If Condition
Answer: B,C
Explanation:
Lookup: This is a control activity that retrieves a dataset from any of the supported data sources and makes it available for use by subsequent activities in the pipeline. You can use a Lookup activity to read File1.txt from storage1 and store its content as an array variable ForEach: This is a control activity that iterates over a collection and executes specified activities in a loop. You can use a ForEach activity to loop over the array variable from the Lookup activity and pass each table name as a parameter to a Copy activity that copies data from DB1 to storage11.
NEW QUESTION # 86
You have an Azure Synapse Analytics dedicated SQL pool.
You need to create a table named FactInternetSales that will be a large fact table in a dimensional model. FactInternetSales will contain 100 million rows and two columns named SalesAmount and OrderQuantity. Queries executed on FactInternetSales will aggregate the values in SalesAmount and OrderQuantity from the last year for a specific product. The solution must minimize the data size and query execution time.
How should you complete the code? 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-tables-overview
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
NEW QUESTION # 87
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