Quick Answer
Option B is the correct choice because it combines mapping data flow’s built-in schema drift handling with PolyBase staging via Blob Storage, enabling automatic adaptation to column changes while keeping latency under 15 minutes and minimizing source impact. The mapping data flow uses a watermark column to query only incremental changes, then stages data in Blob Storage before PolyBase bulk loads into Synapse, which avoids locking the source and handles high transaction volumes efficiently. On the DP-203 exam, this scenario tests your understanding of how to balance low-latency incremental loads with schema evolution—a common trap is choosing Option A, which lacks schema drift support, or Option D, which adds unnecessary complexity with log shipping. Remember that mapping data flows are the go-to when you need both transformation flexibility and automatic schema drift detection in Azure Data Factory. Memory tip: “Map the drift, stage the shift” — mapping data flows handle schema changes, and staging via Blob Storage shifts the load off the source.
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 a data engineer for a global retail company. The company has a hybrid architecture with on-premises SQL Server databases and Azure Synapse Analytics. You need to design a data processing solution that ingests incremental changes from the on-premises SQL Server database (source) into Azure Synapse Analytics (sink) with low latency (under 15 minutes) and high reliability. The source database is 5 TB and experiences high transaction volume during business hours. The solution must minimize impact on the source system and handle schema changes automatically. You have the following options:
Option A: Use Azure Data Factory with a copy activity that uses a watermark column to query incremental changes every 10 minutes. The copy activity writes directly to the Synapse table using PolyBase.
Option B: Use Azure Data Factory with a mapping data flow that reads from the source using a SQL query with a watermark, performs transformations, and writes to Synapse using staging via Blob Storage and PolyBase.
Option C: Use SQL Server Integration Services (SSIS) running on Azure-SSIS Integration Runtime to extract data using change data capture (CDC) and load into Synapse.
Option D: Use Azure Databricks with Auto Loader to ingest files from a staging area that is populated by a separate log-shipping process from the source.
Which option should you choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Option C
Why wrong: SSIS is heavier and may not meet 15-min latency.
Option A
Why wrong: No schema drift handling; direct PolyBase may fail on schema changes.
Option D
Why wrong: Requires additional infrastructure and increases latency.
Option B
Mapping data flow supports schema drift and uses staging for PolyBase.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Option B
Option B is correct because it handles incremental loads with low latency, uses PolyBase for efficient loading, and mapping data flow allows for schema drift handling and transformations without impacting source. Option A lacks schema drift handling. Option C requires SSIS packages and may have higher latency. Option D requires additional log-shipping, increasing complexity and latency.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Option C
Why it's wrong here
SSIS is heavier and may not meet 15-min latency.
Option A
Why it's wrong here
No schema drift handling; direct PolyBase may fail on schema changes.
Option D
Why it's wrong here
Requires additional infrastructure and increases latency.
Option B
Why this is correct
Mapping data flow supports schema drift and uses staging for PolyBase.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
OSPF neighbours must agree on key parameters.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related DP-203 OSPF questions on adjacency and route selection.
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FAQ
Develop data processing — This question tests Develop data processing — OSPF neighbours must agree on key parameters..
The correct answer is: Option B — Option B is correct because it handles incremental loads with low latency, uses PolyBase for efficient loading, and mapping data flow allows for schema drift handling and transformations without impacting source. Option A lacks schema drift handling. Option C requires SSIS packages and may have higher latency. Option D requires additional log-shipping, increasing complexity and latency.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related DP-203 OSPF questions on adjacency and route selection.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 21, 2026
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