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DP-203 · topic practice

Develop data processing practice questions

Develop data processing (46%) covers batch and streaming pipelines in Azure. Expect questions on Azure Databricks, Synapse Spark and serverless SQL pools, Data Factory, Stream Analytics, and Event Hubs. You must choose correct transformations, authentication, windowing, and file formats for ADLS Gen2 and Delta Lake scenarios.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Editorial oversight:Johnson Ajibi· MSc IT Security, IEEE Senior Member
20 questionsDomain: Develop data processing
Practice 10 questionsBrowse domain →

What the exam tests

What to know about Develop data processing

You must design and implement batch and streaming transformations using Databricks, Synapse, Data Factory, and Stream Analytics. The most important thing is selecting the right compute engine, authentication method, and windowing or file format for each ADLS Gen2 scenario.

Joining large Parquet datasets in Azure Databricks with Delta Lake and partitioning

Querying Parquet in Synapse serverless SQL pools using OPENROWSET and managed identity

Transforming semi-structured JSON with Synapse Spark or serverless SQL JSON functions

Streaming aggregation in Stream Analytics with Event Hubs, tumbling windows, and late-arrival tolerance

Watch out for

Common Develop data processing exam traps

  • ▸Assuming serverless SQL pools can use SQL authentication or service principal secrets instead of Azure AD pass-through or managed identity.
  • ▸Forgetting that Stream Analytics late-arrival tolerance affects event ordering and window output, not just dashboard refresh.
  • ▸Using row-by-row processing in Databricks instead of set-based DataFrame operations, causing poor performance on large Parquet joins.

Practice set

Develop data processing questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice
Read the full Develop data processing explanation →

You are designing a data processing pipeline in Azure Synapse Analytics that ingests streaming data from Azure Event Hubs and stores it in a dedicated SQL pool. The data volume is approximately 500 GB per hour with peak spikes. The pipeline must minimize data loss during transient failures. Which feature should you implement?

Question 2mediummultiple choice
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You are designing a data pipeline in Azure Data Factory (ADF) that copies data from an on-premises SQL Server database to Azure Synapse Analytics dedicated SQL pool. The pipeline must run daily and handle incremental loads efficiently. Which sink dataset type and copy method should you use?

Question 3hardmultiple choice
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You are implementing a streaming solution using Azure Stream Analytics. The input is from an IoT Hub receiving telemetry from thousands of devices. The output is to Azure Synapse Analytics dedicated SQL pool. The requirement is to compute rolling averages over a 5-minute tumbling window and write results every minute. Which windowing function and output configuration should you use?

Question 4easymultiple choice
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You are designing a data processing solution using Azure Databricks. The data is stored in Delta Lake format. You need to ensure that when you read the latest version of the table, you only see committed data and not uncommitted transactions. Which isolation level should you use?

Question 5hardmultiple choice
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You are monitoring an Azure Synapse Pipeline that uses a Mapping Data Flow. The data flow processes 2 GB of data from a CSV source and writes to a Delta sink. The pipeline fails with a 'DataFlowException: Operation aborted' error after running for 45 minutes. The cluster is configured with 8 cores. What is the most likely cause?

Question 6mediummultiple choice
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You are working with Azure Synapse Analytics serverless SQL pool. You need to query a set of Parquet files located in ADLS Gen2. The files have nested columns (structs and arrays). Which function should you use to flatten the nested data?

Question 7hardmultiple choice
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You are designing a data processing solution using Azure Databricks with Delta Lake. The data is ingested from multiple sources and needs to be deduplicated based on a composite key (source_id, record_id). New data may have duplicates within the same batch. Which write mode and table property should you use to handle this efficiently?

Question 8mediummultiple choice
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You are configuring a data pipeline in Azure Data Factory that uses a Mapping Data Flow. The source is a SQL Server table with 50 million rows. The sink is a Delta table in ADLS Gen2. The pipeline runs slowly. You need to improve performance by reducing the number of partitions in the data flow. Which setting should you adjust?

Question 9hardmultiple choice
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You are designing a batch processing pipeline in Azure Databricks. The data is stored in Delta Lake and you need to perform a time-series join between two tables: 'events' (100 billion rows) and 'sessions' (10 billion rows). The join condition is on 'device_id' and a timestamp range (event_time BETWEEN session_start AND session_end). Which join strategy would be most efficient?

Question 10mediummultiple choice
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You are monitoring an Azure Data Factory pipeline that runs every hour. The pipeline uses a Copy activity to copy data from Azure SQL Database to Azure Blob Storage. Recently, the pipeline has been failing with a 'Timeout' error. The source SQL database has a large number of records. What should you do to resolve the timeout?

Question 11mediummultiple choice
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You are designing a data processing solution in Azure Synapse Analytics. The solution must support incremental loading of data from an Azure SQL Database to a dedicated SQL pool using PolyBase. Which approach should you use to minimize data movement and maximize performance?

Question 12hardmultiple choice
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You are troubleshooting a pipeline in Azure Data Factory that copies data from an Azure Blob Storage to an Azure Synapse Analytics dedicated SQL pool. The pipeline fails with the error: 'PolyBase requires a varchar(max) column to be less than 1 MB.' Which action should you take to resolve this issue?

Question 13mediummultiple choice
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You are developing a data processing pipeline in Azure Synapse Analytics. The pipeline uses a mapping data flow to transform data from Azure Data Lake Storage Gen2 to a dedicated SQL pool. The data flow includes a Derived Column transformation that uses the expression: `iif(isnull(Column1), 'Default', Column1)`. However, the transformation is not handling NULL values correctly. What is the most likely cause?

Question 14hardmultiple choice
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You are designing a data processing solution in Azure Synapse Analytics. The solution must use a serverless SQL pool to query data in Azure Data Lake Storage Gen2. The data is stored in Delta Lake format. Which of the following statements is true regarding querying Delta Lake tables with serverless SQL pool?

Question 15mediummultiple choice
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You are developing a data processing pipeline in Azure Data Factory. The pipeline must copy data from an on-premises SQL Server to Azure Blob Storage. The data volumes are large, and the network connection is unreliable. Which configuration should you use to ensure resilience and resume capability?

Question 16mediummulti select
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You are designing a data processing solution in Azure Synapse Analytics. The solution must use a serverless SQL pool to query data in Azure Data Lake Storage Gen2. The data is stored as Parquet files partitioned by date. Which TWO of the following statements are true regarding querying this data? (Select TWO.)

Question 17hardmulti select
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You are developing a data processing pipeline in Azure Data Factory. The pipeline uses a mapping data flow to transform data. You need to ensure that the data flow can handle schema drift from the source. Which THREE of the following actions should you take? (Select THREE.)

Question 18easymulti select
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You are designing a data processing solution in Azure Synapse Analytics. The solution must use a dedicated SQL pool to support both batch and near-real-time data ingestion. Which TWO of the following methods can you use to ingest data into a dedicated SQL pool? (Select TWO.)

Question 19hardmultiple choice
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Refer to the exhibit. You have an Azure Data Factory dataset definition for a Parquet file stored in Azure Data Lake Storage Gen2. You attempt to use this dataset as a source in a copy activity, but the copy activity fails with an error indicating that the file is not found. The file 'sales_orders.parquet' exists at the specified path. What is the most likely cause of the error?

Exhibit

Refer to the exhibit.

{
  "name": "sales_orders",
  "properties": {
    "folder": "orders",
    "type": "AzureBlobFSLocation",
    "linkedServiceName": {
      "referenceName": "ADLSGen2",
      "type": "LinkedServiceReference"
    },
    "typeProperties": {
      "fileName": "sales_orders.parquet",
      "folderPath": "data/orders/year=2023/month=01/day=15/"
    },
    "compressionCodec": "snappy",
    "columnDelimiter": ","
  }
}
Question 20easymultiple choice
Read the full Develop data processing explanation →

Refer to the exhibit. You have created an external table in Azure Synapse Analytics serverless SQL pool to query Parquet files stored in Azure Data Lake Storage Gen2. When you query the external table, you get an error that the external table is not accessible. What should you check first?

Exhibit

Refer to the exhibit.

CREATE EXTERNAL DATA SOURCE myDataSource
WITH (
    LOCATION = 'https://mystorageaccount.dfs.core.windows.net/mycontainer'
);

CREATE EXTERNAL FILE FORMAT myFileFormat
WITH (
    FORMAT_TYPE = PARQUET,
    DATA_COMPRESSION = 'org.apache.hadoop.io.compress.SnappyCodec'
);

CREATE EXTERNAL TABLE dbo.Sales (
    SaleID int,
    ProductName varchar(100),
    SaleDate date,
    Amount decimal(10,2)
)
WITH (
    LOCATION = 'sales/',
    DATA_SOURCE = myDataSource,
    FILE_FORMAT = myFileFormat
);
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Frequently asked questions

What does the DP-203 exam test about Develop data processing?
You must design and implement batch and streaming transformations using Databricks, Synapse, Data Factory, and Stream Analytics. The most important thing is selecting the right compute engine, authentication method, and windowing or file format for each ADLS Gen2 scenario.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Develop data processing questions in a focused session?
Yes — the session launcher on this page draws every question from the Develop data processing domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other DP-203 topics?
Use the topic links above to move to related areas, or go back to the DP-203 question bank to see all topics.
Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the DP-203 exam covers. They are not copied from any real exam or dump site.