Data Engineering on Microsoft Azure (DP-203) Exam Practice Questions & Study Guide 2026

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What is DP-203 — Data Engineering on Microsoft Azure?

The DP-203 exam is a Microsoft certification that validates your skills and knowledge as a Data Engineering on Microsoft Azure professional. Whether you're looking to advance your career, get a pay raise, or simply prove your Azure expertise — this certification is your next big step.

Passing DP-203 tells employers one thing — you know your stuff. And with the cloud industry growing faster than ever in 2026, Azure certified professionals are in higher demand than ever before.

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How hard is the DP-203 exam?

DP-203 is a challenging exam — but with the right preparation it's absolutely passable on your first attempt. Our practice tests are designed to match the exact difficulty level of the real exam so there are no surprises on exam day.

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The DP-203 exam typically contains between 40 to 60 questions. Our practice tests cover all question types including multiple choice, drag and drop, and case studies.

Are 2026 practice questions updated?

Yes! Dumps4Azure’s practice questions are aligned with the latest 2026 exam objectives, ensuring your preparation is relevant.

How long should I study for DP-203?

Most professionals pass DP-203 after 2 to 4 weeks of focused preparation using our practice tests. The more consistently you practice, the faster you'll be ready.

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Microsoft DP-203 Sample Questions

Question 1

Note: This question is part of a series of questions that present the same scenario.Each question in the series contains a unique solution that might meet the statedgoals. Some question sets might have more than one correct solution, while othersmight not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As aresult, these questions will not appear in the review screen.You have an Azure Data Lake Storage account that contains a staging zone.You need to design a daily process to ingest incremental data from the staging zone,transform the data by executing an R script, and then insert the transformed data into adata warehouse in Azure Synapse Analytics.Solution: You schedule an Azure Databricks job that executes an R notebook, and theninserts the data into the data warehouse.Does this meet the goal?

A. Yes
B. No

Answer: B
Explanation:
Must use an Azure Data Factory, not an Azure Databricks job.
Reference:
https://docs.microsoft.com/en-US/azure/data-factory/transform-data

Question 2

You plan to use an Apache Spark pool in Azure Synapse Analytics to load data to an AzureData Lake Storage Gen2 account.You need to recommend which file format to use to store the data in the Data Lake Storageaccount. The solution must meet the following requirements:• Column names and data types must be defined within the files loaded to the Data LakeStorage account.• Data must be accessible by using queries from an Azure Synapse Analytics serverlessSQL pool.• Partition elimination must be supported without having to specify a specific partition.What should you recommend?

A. Delta Lake
B. JSON
C. CSV
D. ORC

Answer: D

Question 3

You are designing 2 solution that will use tables in Delta Lake on Azure Databricks.You need to minimize how long it takes to perform the following:*Queries against non-partitioned tables* Joins on non-partitioned columnsWhich two options should you include in the solution? Each correct answer presents part ofthe solution.(Choose Correct Answer and Give Explanation and References to Support the answersbased from Data Engineering on Microsoft Azure)

A. Z-Ordering
B. Apache Spark caching
C. dynamic file pruning (DFP)
D. the clone command

Answer: A,C
Explanation: According to the information I found on the web, two options that you should
include in the solution to minimize how long it takes to perform queries and joins on nonpartitioned
tables are:
Z-Ordering: This is a technique to colocate related information in the same set of
files. This co-locality is automatically used by Delta Lake in data-skipping
algorithms. This behavior dramatically reduces the amount of data that Delta Lake
on Azure Databricks needs to read123.
Apache Spark caching: This is a feature that allows you to cache data in memory
or on disk for faster access. Caching can improve the performance of repeated
queries and joins on the same data. You can cache Delta tables using the CACHE
TABLE or CACHE LAZY commands. To minimize the time it takes to perform queries against non-partitioned tables and joins on
non-partitioned columns in Delta Lake on Azure Databricks, the following options should be
included in the solution:
A. Z-Ordering: Z-Ordering improves query performance by co-locating data that share the
same column values in the same physical partitions. This reduces the need for shuffling
data across nodes during query execution. By using Z-Ordering, you can avoid full table
scans and reduce the amount of data processed.
B. Apache Spark caching: Caching data in memory can improve query performance by
reducing the amount of data read from disk. This helps to speed up subsequent queries
that need to access the same data. When you cache a table, the data is read from the data
source and stored in memory. Subsequent queries can then read the data from memory,
which is much faster than reading it from disk.
References:
Delta Lake on Databricks: https://docs.databricks.com/delta/index.html
Best Practices for Delta Lake on
Databricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta-lakeon-
databricks.html

Question 4

You have an Azure subscription that contains an Azure Blob Storage account namedstorage1 and an Azure Synapse Analytics dedicated SQL pool named Pool1.You need to store data in storage1. The data will be read by Pool1. The solution must meetthe following requirements:Enable Pool1 to skip columns and rows that are unnecessary in a query.Automatically create column statistics.Minimize the size of files.Which type of file should you use?

A. JSON
B. Parquet
C. Avro
D. CSV

Answer: B
Explanation:
Automatic creation of statistics is turned on for Parquet files. For CSV files, you need to
create statistics manually until automatic creation of CSV files statistics is supported.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-statisti

Question 5

You have an Azure Databricks workspace that contains a Delta Lake dimension tablenamed Tablet. Table1 is a Type 2 slowly changing dimension (SCD) table. You need toapply updates from a source table to Table1. Which Apache Spark SQL operation shouldyou use?

A. CREATE
B. UPDATE
C. MERGE
D. ALTER

Answer: C
Explanation:
The Delta provides the ability to infer the schema for data input which further reduces the
effort required in managing the schema changes. The Slowly Changing Data(SCD) Type 2
records all the changes made to each key in the dimensional table. These operations
require updating the existing rows to mark the previous values of the keys as old and then
inserting new rows as the latest values. Also, Given a source table with the updates and
the target table with dimensional data, SCD Type 2 can be expressed with the merge.
Example:
// Implementing SCD Type 2 operation using merge function
customersTable
as("customers")
merge(
stagedUpdates.as("staged_updates"),
"customers.customerId = mergeKey")
whenMatched("customers.current = true AND customers.address <>
staged_updates.address")
updateExpr(Map(
"current" -> "false",
"endDate" -> "staged_updates.effectiveDate"))
whenNotMatched() insertExpr(Map(
"customerid" -> "staged_updates.customerId",
"address" -> "staged_updates.address",
"current" -> "true",
"effectiveDate" -> "staged_updates.effectiveDate",
"endDate" -> "null"))
execute()
}
Reference:
https://www.projectpro.io/recipes/what-is-slowly-changing-data-scd-type-2-operation-deltatable-
databricks

Question 6

You have an Azure Synapse Analytics dedicated SQL pool named Pool1. Pool1 contains atable named table1.You load 5 TB of data intotable1.You need to ensure that columnstore compression is maximized for table1.Which statement should you execute?

A. ALTER INDEX ALL on table1 REORGANIZE
B. ALTER INDEX ALL on table1 REBUILD
C. DBCC DBREINOEX (table1)
D. DBCC INDEXDEFRAG (pool1,tablel)

Answer: B
Explanation:
Columnstore and columnstore archive compression
Columnstore tables and indexes are always stored with columnstore compression. You can
further reduce the size of columnstore data by configuring an additional compression called
archival compression. To perform archival compression, SQL Server runs the Microsoft
XPRESS compression algorithm on the data. Add or remove archival compression by
using the following data compression types:
Use COLUMNSTORE_ARCHIVE data compression to compress columnstore data with
archival compression.
Use COLUMNSTORE data compression to decompress archival compression. The
resulting data continue to be compressed with columnstore compression.
To add archival compression, use ALTER TABLE (Transact-SQL) or ALTER INDEX
(Transact-SQL) with the REBUILD option and DATA COMPRESSION =
COLUMNSTORE_ARCHIVE. Reference: https://learn.microsoft.com/en-us/sql/relational-databases/datacompression/
data-compression

Question 7

You have two Azure Blob Storage accounts named account1 and account2?You plan to create an Azure Data Factory pipeline that will use scheduled intervals toreplicate newly created or modified blobs from account1 to account?You need to recommend a solution to implement the pipeline. The solution must meet thefollowing requirements:• Ensure that the pipeline only copies blobs that were created of modified since the mostrecent replication event.• Minimize the effort to create the pipeline. What should you recommend?

A. Create a pipeline that contains a flowlet.
B. Create a pipeline that contains a Data Flow activity.
C. Run the Copy Data tool and select Metadata-driven copy task.
D. Run the Copy Data tool and select Built-in copy task.

Answer: A

Question 8

You have an Azure Data Factory pipeline named pipeline1 that is invoked by a tumblingwindow trigger named Trigger1. Trigger1 has a recurrence of 60 minutes.You need to ensure that pipeline1 will execute only if the previous execution completessuccessfully.How should you configure the self-dependency for Trigger1?

A. offset: "-00:01:00" size: "00:01:00"
B. offset: "01:00:00" size: "-01:00:00"
C. offset: "01:00:00" size: "01:00:00"
D. offset: "-01:00:00" size: "01:00:00"

Answer: D
Explanation:
Tumbling window self-dependency properties
In scenarios where the trigger shouldn't proceed to the next window until the preceding
window is successfully completed, build a self-dependency. A self-dependency trigger
that's dependent on the success of earlier runs of itself within the preceding hour will have
the properties indicated in the following code.
Example code:
"name": "DemoSelfDependency",
"properties": {
"runtimeState": "Started",
"pipeline": {
"pipelineReference": {
"referenceName": "Demo",
"type": "PipelineReference"
}
},
"type": "TumblingWindowTrigger",
"typeProperties": {
"frequency": "Hour",
"interval": 1,
"startTime": "2018-10-04T00:00:00Z",
"delay": "00:01:00",
"maxConcurrency": 50,
"retryPolicy": {
"intervalInSeconds": 30
},
"dependsOn": [
{
"type": "SelfDependencyTumblingWindowTriggerReference",
"size": "01:00:00",
"offset": "-01:00:00"
}
]
}
}
}
Reference: https://docs.microsoft.com/en-us/azure/data-factory/tumbling-window-triggerdependency

Question 9

You are building a data flow in Azure Data Factory that upserts data into a table in anAzure Synapse Analytics dedicated SQL pool.You need to add a transformation to the data flow. The transformation must specify logicindicating when a row from the input data must be upserted into the sink.Which type of transformation should you add to the data flow?

A. join
B. select
C. surrogate key
D. alter row

Answer: D
Explanation:
The alter row transformation allows you to specify insert, update, delete, and upsert
policies on rows based on expressions. You can use the alter row transformation to
perform upserts on a sink table by matching on a key column and setting the appropriate
row policy

Question 10

You have an Azure Data lake Storage account that contains a staging zone.You need to design a daily process to ingest incremental data from the staging zone,transform the data by executing an R script, and then insert the transformed data into adata warehouse in Azure Synapse Analytics.Solution: You use an Azure Data Factory schedule trigger to execute a pipeline thatexecutes an Azure Databricks notebook, and then inserts the data into the datawarehouse.Dow this meet the goal?

A. Yes
B. No

Answer: B
Explanation:
If you need to transform data in a way that is not supported by Data Factory, you can
create a custom activity, not an Azure Databricks notebook, with your own data processing
logic and use the activity in the pipeline. You can create a custom activity to run R scripts
on your HDInsight cluster with R installed.
Reference:
https://docs.microsoft.com/en-US/azure/data-factory/transform-data

Microsoft DP-203 Exam Reviews

    Auden         Jun 06, 2026

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Great experience using Dumps4Azure for the DP-203 exam. The questions covered all major Azure data engineering concepts.

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DP-203 Dumps

Data Engineering on Microsoft Azure (DP-203) Exam Practice Questions & Study Guide 2026

What is DP-203 — Data Engineering on Microsoft Azure?

Everything You Need to Pass DP-203 — In One Place

Why Thousands of Professionals Choose Dumps4Azure for DP-203?

How hard is the DP-203 exam?

How many questions are in the DP-203 exam?

Are 2026 practice questions updated?

How long should I study for DP-203?

Do you offer a free demo for DP-203?

What if I fail DP-203 after using your material?

Dump4Azure Exam Package – PDF vs Testing Engine

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