The answer is to use a Mapping Data Flow with derived column transformations to mask sensitive columns. This approach is correct because Mapping Data Flows in Azure Data Factory allow you to visually design data transformation logic, including applying masking functions like substring, replace, or custom expressions directly on columns during the ingestion pipeline, ensuring sensitive data is obfuscated before it lands in the staging area. On the DP-203 exam, this question tests your understanding of when to use native data flow transformations versus other Azure services—a common trap is confusing runtime masking (like Azure SQL Dynamic Data Masking, which only hides data at query time) with ETL-time masking, or misapplying governance tools like Purview or compliance policies. Remember the key distinction: if the requirement is to alter data during movement, you need a transformation step in the pipeline, not a post-ingestion feature. Memory tip: “Mask in the flow, not at the show”—meaning apply masking during the data flow, not at query display time.
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
This DP-203 practice question tests your understanding of secure, monitor, and optimize data storage and 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.
An organization is using Azure Data Factory to ingest data from multiple on-premises SQL Server databases into Azure Synapse Analytics. They need to ensure that sensitive data is masked during ingestion before landing in the staging area. What is the best approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a Mapping Data Flow with derived column transformations to mask sensitive columns.
Option D is correct because Data Flow allows transformation steps including column masking. Option A is wrong because Azure SQL Database dynamic data masking is applied at query time, not during copy. Option B is wrong because Azure Purview is for governance, not data masking. Option C is wrong because Azure Policy is for compliance, not data masking at source.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Apply an Azure Policy that masks sensitive data in Azure Synapse Analytics.
Why it's wrong here
Azure Policy enforces compliance rules, not data masking during ingestion.
✗
Use Azure SQL Database dynamic data masking on the source databases.
Why it's wrong here
Dynamic data masking is applied at query time; it does not mask data during extraction.
✓
Use a Mapping Data Flow with derived column transformations to mask sensitive columns.
Why this is correct
Mapping Data Flow allows you to apply transformations like mask using derived columns before writing to staging.
Clue confirmation
The clue word "best" in the question point toward this answer.
Use Azure Purview to classify and mask sensitive data automatically.
Why it's wrong here
Azure Purview does not mask data during ingestion; it classifies and manages metadata.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
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
How to think about this question
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.
KKey Concepts to Remember
OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.
TExam Day Tips
→Check area mismatch first when OSPF adjacency fails.
→Review passive interfaces when a network is advertised but no neighbour forms.
→Use show ip ospf neighbor and show ip route clues carefully.
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
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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.
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.
Secure, monitor, and optimize data storage and data processing — This question tests Secure, monitor, and optimize data storage and data processing — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Use a Mapping Data Flow with derived column transformations to mask sensitive columns. — Option D is correct because Data Flow allows transformation steps including column masking. Option A is wrong because Azure SQL Database dynamic data masking is applied at query time, not during copy. Option B is wrong because Azure Purview is for governance, not data masking. Option C is wrong because Azure Policy is for compliance, not data masking at source.
What should I do if I get this DP-203 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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 data pipeline that uses Azure Data Factory to copy data from an Azure SQL database to Azure Data Lake Storage Gen2. The data contains personally identifiable information (PII) that must be masked. Which Data Factory feature should you use?
easy
A.Use a copy activity with a query to select only non-PII columns.
B.Use a stored procedure activity to mask data in the source before copy.
C.Enable staging on the copy activity to use PolyBase.
✓ D.Use a mapping data flow to apply a mask transformation on PII columns.
Why D: Option B is correct because Data Factory data flows allow column-level transformations including data masking. Option A is wrong because copy activity only copies data without transformation. Option C is wrong because stored procedures run on the source, not during copy. Option D is wrong because staging is for bulk copy performance, not masking.
Last reviewed: Jun 21, 2026
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