DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
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?
Answer choices
Why each option matters
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 to apply a mask transformation on PII columns.
Mapping Data Flows in Azure Data Factory provide built-in transformations for data masking, such as the Mask transformation, which can obfuscate PII columns during the data flow. Option A is incorrect because a copy activity with a query can only filter columns but does not support masking; it simply selects a subset of columns without transformation. Option B is incorrect because while a stored procedure activity can execute masking logic on the source, it requires additional setup and does not integrate seamlessly with Data Factory's native transformation capabilities. Option C is incorrect because staging with PolyBase is used to improve bulk copy performance, not for data masking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a copy activity with a query to select only non-PII columns.
Why it's wrong here
This omits columns but does not mask them.
- ✗
Use a stored procedure activity to mask data in the source before copy.
Why it's wrong here
This modifies source data, which may not be desired.
- ✗
Enable staging on the copy activity to use PolyBase.
Why it's wrong here
Staging does not provide masking.
- ✓
Use a mapping data flow to apply a mask transformation on PII columns.
Why this is correct
Data flows support mask transformations.
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Variation 1. 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?
hard- A.Apply an Azure Policy that masks sensitive data in Azure Synapse Analytics.
- B.Use Azure SQL Database dynamic data masking on the source databases.
- ✓ C.Use a Mapping Data Flow with derived column transformations to mask sensitive columns.
- D.Use Azure Purview to classify and mask sensitive data automatically.
Why C: Mapping Data Flow in Azure Data Factory allows you to transform data during ingestion. Using derived column transformations, you can apply masking functions (e.g., substituting characters, hashing) to sensitive columns before writing to the staging area. This approach masks data before it reaches the staging area, meeting the requirement. Option A (Azure Policy) is for compliance and cannot mask data. Option B (Dynamic Data Masking on source) masks data at query time, not during ingestion. Option D (Azure Purview) is for data governance and classification, not for masking data during a pipeline.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.