2025 Valid DP-203 Real Exam Questions, practice Microsoft Certified: Azure Data Engineer Associate
Latest Success Metrics For Actual DP-203 Exam (Updated 365 Questions)
Data engineering is a crucial field that involves the development, testing, and maintenance of architectures, algorithms, and systems for capturing, storing, processing, and analyzing data. The Microsoft DP-203 certification exam is designed to test the candidate's knowledge of data engineering concepts and tools, including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, and Azure Stream Analytics. DP-203 exam is essential for data engineers who want to advance their careers in the field and stay up-to-date with the latest Azure technologies.
NEW QUESTION # 84
You have an Azure Data Lake Storage Gen2 account named account1 that stores logs as shown in the following table.
You do not expect that the logs will be accessed during the retention periods.
You need to recommend a solution for account1 that meets the following requirements:
Automatically deletes the logs at the end of each retention period
Minimizes storage costs
What should you include in the recommendation? 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/access-tiers-overview
NEW QUESTION # 85
You have an Azure Stream Analytics job that is a Stream Analytics project solution in Microsoft Visual Studio. The job accepts data generated by IoT devices in the JSON format.
You need to modify the job to accept data generated by the IoT devices in the Protobuf format.
Which three actions should you perform from Visual Studio on sequence? 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/stream-analytics/custom-deserializer
NEW QUESTION # 86
You are designing an Azure Stream Analytics solution that receives instant messaging data from an Azure Event Hub.
You need to ensure that the output from the Stream Analytics job counts the number of messages per time zone every 15 seconds.
How should you complete the Stream Analytics query? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION # 87
A company plans to use Platform-as-a-Service (PaaS) to create the new data pipeline process. The process must meet the following requirements:
Ingest:
Access multiple data sources.
Provide the ability to orchestrate workflow.
Provide the capability to run SQL Server Integration Services packages.
Store:
Optimize storage for big data workloads.
Provide encryption of data at rest.
Operate with no size limits.
Prepare and Train:
Provide a fully-managed and interactive workspace for exploration and visualization.
Provide the ability to program in R, SQL, Python, Scala, and Java.
Provide seamless user authentication with Azure Active Directory.
Model & Serve:
Implement native columnar storage.
Support for the SQL language
Provide support for structured streaming.
You need to build the data integration pipeline.
Which technologies should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 88
You have two Azure Storage accounts named Storage1 and Storage2. Each account holds one container and has the hierarchical namespace enabled. The system has files that contain data stored in the Apache Parquet format.
You need to copy folders and files from Storage1 to Storage2 by using a Data Factory copy activity. The solution must meet the following requirements:
No transformations must be performed.
The original folder structure must be retained.
Minimize time required to perform the copy activity.
How should you configure the copy activity? 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/data-factory/format-parquet
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage
NEW QUESTION # 89
You are designing a slowly changing dimension (SCD) for supplier data in an Azure Synapse Analytics dedicated SQL pool.
You plan to keep a record of changes to the available fields.
The supplier data contains the following columns.
Which three additional columns should you add to the data to create a Type 2 SCD? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. foreign key
- B. effective end date
- C. business key
- D. effective start date
- E. surrogate primary key
- F. last modified date
Answer: B,C,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/integration-services/data-flow/transformations/slowly-changing-dimension-
NEW QUESTION # 90
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:
Reference:
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema
NEW QUESTION # 91
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html
NEW QUESTION # 92
You have an Azure Databricks resource.
You need to log actions that relate to changes in compute for the Databricks resource.
Which Databricks services should you log?
- A. SSH
- B. DBFS
- C. workspace
- D. clusters
Answer: C
Explanation:
E jobs
Explanation:
Cloud Provider Infrastructure Logs. Databricks logging allows security and admin teams to demonstrate conformance to data governance standards within or from a Databricks workspace. Customers, especially in the regulated industries, also need records on activities like: - User access control to cloud data storage - Cloud Identity and Access Management roles - User access to cloud network and compute Azure Databricks offers three distinct workloads on several VM Instances tailored for your data analytics workflow-the Jobs Compute and Jobs Light Compute workloads make it easy for data engineers to build and execute jobs, and the All-Purpose Compute workload makes it easy for data scientists to explore, visualize, manipulate, and share data and insights interactively.
NEW QUESTION # 93
You have an Azure Data Factory pipeline shown the following exhibit.
The execution log for the first pipeline run is shown in the following exhibit.
The execution log for the second pipeline run is shown in the following exhibit.
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:
NEW QUESTION # 94
You have an Azure Synapse serverless SQL pool.
You need to read JSON documents from a file by using the OPENROWSET function.
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:
Explanation:
NEW QUESTION # 95
You need to output files from Azure Data Factory.
Which file format should you use for each type of output? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Parquet
Parquet stores data in columns, while Avro stores data in a row-based format. By their very nature, column-oriented data stores are optimized for read-heavy analytical workloads, while row-based databases are best for write-heavy transactional workloads.
Box 2: Avro
An Avro schema is created using JSON format.
AVRO supports timestamps.
Note: Azure Data Factory supports the following file formats (not GZip or TXT).
* Avro format
* Binary format
* Delimited text format
* Excel format
* JSON format
* ORC format
* Parquet format
* XML format
Reference:
https://www.datanami.com/2018/05/16/big-data-file-formats-demystified
NEW QUESTION # 96
You are developing a solution that will stream to Azure Stream Analytics. The solution will have both streaming data and reference data.
Which input type should you use for the reference data?
- A. Azure IoT Hub
- B. Azure Cosmos DB
- C. Azure Blob storage
- D. Azure Event Hubs
Answer: C
Explanation:
Stream Analytics supports Azure Blob storage and Azure SQL Database as the storage layer for Reference Data.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
NEW QUESTION # 97
You plan to create a table in an Azure Synapse Analytics dedicated SQL pool.
Data in the table will be retained for five years. Once a year, data that is older than five years will be deleted.
You need to ensure that the data is distributed evenly across partitions. The solution must minimize the amount of time required to delete old data.
How should you complete the Transact-SQL statement? 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:
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool
NEW QUESTION # 98
You have a Microsoft Purview account. The Lineage view of a CSV file is shown in the following exhibit.
How is the data for the lineage populated?
- A. by scanning data stores
- B. manually
- C. by executing a Data Factory pipeline
Answer: A
Explanation:
According to Microsoft Purview Data Catalog lineage user guide1, data lineage in Microsoft Purview is a core platform capability that populates the Microsoft Purview Data Map with data movement and transformations across systems2. Lineage is captured as it flows in the enterprise and stitched without gaps irrespective of its source2.
NEW QUESTION # 99
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. To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.
- B. To the data flow, add a sink transformation to write the rows to a file in blob storage.
- C. Add a select transformation to select only the rows that will cause truncation errors.
- D. To the data flow, add a filter transformation to filter out rows that will cause truncation errors.
Answer: A,B
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 # 100
You plan to monitor an Azure data factory by using the Monitor & Manage app.
You need to identify the status and duration of activities that reference a table in a source database.
Which three actions should you perform in sequence? To answer, move the actions from the list of actions to the answer are and arrange them in the correct order.
Answer:
Explanation:
1 - From the Data Factory authoring UI, generate a user property for Source on all activities.
2 - From the Data Factory monitoring app, add the Source user property to Activity Runs table.
3 - From the Data Factory authoring UI, publish the pipelines
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-visually
NEW QUESTION # 101
You have an Azure subscription that is linked to a hybrid Azure Active Directory (Azure AD) tenant. The subscription contains an Azure Synapse Analytics SQL pool named Pool1.
You need to recommend an authentication solution for Pool1. The solution must support multi-factor authentication (MFA) and database-level authentication.
Which authentication solution or solutions should you include in the recommendation? 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-authentication
NEW QUESTION # 102
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
* Status: Running
* Type: Self-Hosted
* Version: 4.4.7292.1
* Running / Registered Node(s): 1/1
* High Availability Enabled: False
* Linked Count: 0
* Queue Length: 0
* Average Queue Duration. 0.00s
The integration runtime has the following node details:
* Name: X-M
* Status: Running
* Version: 4.4.7292.1
* Available Memory: 7697MB
* CPU Utilization: 6%
* Network (In/Out): 1.21KBps/0.83KBps
* Concurrent Jobs (Running/Limit): 2/14
* Role: Dispatcher/Worker
* Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: fail until the node comes back online
We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.
Box 2: lowered
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%
Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime
NEW QUESTION # 103
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 # 104
You have a SQL pool in Azure Synapse that contains a table named dbo.Customers. The table contains a column name Email.
You need to prevent nonadministrative users from seeing the full email addresses in the Email column. The users must see values in a format of [email protected] instead.
What should you do?
- A. From Microsoft SQL Server Management Studio, set an email mask on the Email column.
- B. From the Azure portal, set a sensitivity classification of Confidential for the Email column.
- C. From Microsoft SQL Server Management studio, grant the SELECT permission to the users for all the columns in the dbo.Customers table except Email.
- D. From the Azure portal, set a mask on the Email column.
Answer: A
Explanation:
From Microsoft SQL Server Management Studio, set an email mask on the Email column. This is because "This feature cannot be set using portal for Azure Synapse (use PowerShell or REST API) or SQL Managed Instance." So use Create table statement with Masking e.g. CREATE TABLE Membership (MemberID int IDENTITY PRIMARY KEY, FirstName varchar(100) MASKED WITH (FUNCTION = 'partial(1,"XXXXXXX",0)') NULL, . . https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview upvoted 24 times
NEW QUESTION # 105
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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 (Data Engineering on Microsoft Azure) certification exam is an industry-recognized credential that validates a candidate's knowledge and skills in the field of data engineering on Azure. DP-203 exam is designed for professionals who want to demonstrate their expertise in designing and implementing data solutions on the Azure platform. DP-203 exam tests the candidate's ability to work with various data technologies such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics.
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