DP-203 Develop data processing Practice Question
Which TWO techniques can you use to handle schema drift in Azure Data Factory mapping data flows?
⚠ Common exam trap
Watch out — candidates often confuse 'handling schema drift' with 'ignoring or rejecting unknown columns' (options D and E), or they think manual column-by-column handling (option B) is a valid technique, when in fact ADF provides automated drift handling through the source setting and pattern matching.
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
✓
Enable 'Allow schema drift' in the source transformation
Option A is correct because enabling 'Allow schema drift' on the source transformation in a mapping data flow lets the flow read columns that are not defined in the dataset schema at design time, so newly arriving columns flow through instead of being dropped. Option C is correct because column pattern matching (rule-based mapping) lets you define rules such as name or type patterns so that columns with similar names are automatically mapped without editing the flow for every new column. Option B is not appropriate because manually adding a derived column for each new column defeats the purpose of automated drift handling and requires redeployment for every schema change. Option D is wrong because assertion rules validate row conditions and can fail or reject rows, but they do not adapt the flow to new or renamed columns. Option E is wrong because a fixed schema mapping ignores unknown columns, which is the opposite of handling schema drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable 'Allow schema drift' in the source transformation
Why this is correct
Enabling 'Allow schema drift' in the source transformation lets mapping data flows read columns absent from the defined schema and carry them through the pipeline, directly satisfying the requirement to handle drifting source structures without failure.
- ✗
Use derived column transformation to handle each new column manually
Why it's wrong here
Derived column expressions reference named columns, so each new field must be added by hand, which defeats drift handling. It is tempting because derived columns genuinely transform or rename known fields, and would be right when the incoming schema is stable and you need computed values.
- ✓
Use column pattern matching to automatically map columns with similar names
Why this is correct
Column pattern matching in mapping data flows maps incoming columns by name rules, so newly appearing or renamed source columns are handled without manual remapping. This directly addresses schema drift, where source structure changes between runs.
- ✗
Use assertion rules to reject rows with unknown columns
Why it's wrong here
Assertion rules validate row content and route failing rows to an error stream; they do not reconcile unknown columns into the sink schema, which is what schema drift demands. They are tempting for data-quality enforcement, but drift handling requires allowing drifted columns in the mapping.
- ✗
Use a fixed schema mapping to ignore unknown columns
Why it's wrong here
A fixed schema mapping discards columns absent from the defined projection, so drifted fields are silently dropped rather than accommodated. It is tempting because explicit mappings are correct when the source schema is contractually fixed and unknown columns must be rejected.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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