The answer is Azure Data Factory with change tracking. This is the correct choice because it leverages SQL Server’s built-in change tracking feature to capture row-level inserts, updates, and deletes without requiring timestamp columns or any schema modifications to the source tables. For the Microsoft Azure Data Engineer Associate DP-203 exam, this scenario tests your understanding of incremental loading strategies when source constraints exist—a common trap is reaching for timestamp-based CDC or third-party tools, but the key insight is that SQL Server change tracking operates transparently at the table level. Remember the mnemonic “No Timestamp? Track Changes” to recall that Azure Data Factory’s change tracking is purpose-built for exactly this constraint. This approach reliably feeds incremental data into Azure Synapse Analytics, making it the ideal solution for the exam’s typical “on-prem SQL Server to Synapse” CDC question.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are designing a change data capture (CDC) solution to incrementally load data from an on-premises SQL Server database to Azure Synapse Analytics. The source tables have no timestamp columns and you cannot modify the schema. Which Azure service should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Factory with change tracking
Azure Data Factory's change tracking capability is the correct choice because it can capture row-level inserts, updates, and deletes from SQL Server without requiring timestamp columns or schema modifications. It uses SQL Server's built-in change tracking feature, which tracks changes at the table level and provides a reliable incremental load mechanism to Azure Synapse Analytics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Azure Synapse Pipelines with mapping data flows
Why it's wrong here
Synapse Pipelines can do CDC but ADF is the more common and flexible choice.
✓
Azure Data Factory with change tracking
Why this is correct
ADF can enable change tracking on SQL Server or use custom watermark logic.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Databricks with Auto Loader
Why it's wrong here
Auto Loader requires file-based sources or change data feed.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is for real-time streaming, not batch CDC.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a timestamp column is mandatory for incremental loads, but Azure Data Factory's change tracking connector bypasses this requirement by using SQL Server's built-in change tracking mechanism, which does not require any schema modifications.
Detailed technical explanation
How to think about this question
SQL Server change tracking works by logging the primary key and operation type (insert, update, delete) for each changed row in internal change tables, which can be queried using the CHANGETABLE function. Azure Data Factory's change tracking connector automatically manages the synchronization version and retrieves only the changes since the last run, making it ideal for CDC without schema changes. In a real-world scenario, this approach avoids the overhead of triggers or timestamps and works even with tables that have no natural watermark column.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Data Factory with change tracking — Azure Data Factory's change tracking capability is the correct choice because it can capture row-level inserts, updates, and deletes from SQL Server without requiring timestamp columns or schema modifications. It uses SQL Server's built-in change tracking feature, which tracks changes at the table level and provides a reliable incremental load mechanism to Azure Synapse Analytics.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are designing a change data capture (CDC) pipeline to ingest incremental changes from an on-premises SQL Server database into Azure Data Lake Storage Gen2. The pipeline must run every 5 minutes and handle high-volume DML changes. Which Azure service should you use to capture the changes with low latency?
medium
A.Azure Data Share to share the SQL Server data and capture changes.
✓ B.Azure Data Factory with a change data capture (CDC) source in the mapping data flow.
C.Azure Synapse Pipelines with a copy activity that uses a query to capture changes.
D.Azure Databricks with Auto Loader and Delta Live Tables to capture changes.
Why B: Azure Data Factory's mapping data flow includes a native CDC source that can connect to SQL Server and capture incremental DML changes (inserts, updates, deletes) with low latency. This approach uses change tracking or change data capture features in SQL Server to identify changes, and the pipeline can run every 5 minutes to meet the high-volume requirement without custom coding.
Last reviewed: Jun 24, 2026
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