DP-700 Ingest and Transform Data Practice Question
Exhibit
{
"source": "RawLogs",
"sink": "ProcessedDelta",
"transformation": "filter",
"schema_validation": "strict"
}Refer to the exhibit. You are configuring a Dataflow Gen2 to move data. Based on the JSON configuration, what happens if the incoming data contains an extra column not defined in the destination schema?
⚠ Common exam trap
Candidates often assume that Dataflow Gen2 will automatically ignore or append unexpected columns, failing to recognize that 'strict' schema validation is a binary pass-or-fail mechanism that prevents any schema deviation.
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
✓
The ingestion process fails.
With schema validation set to 'strict', Dataflow Gen2 enforces a rigid mapping between source and sink schemas. If the incoming data does not match the defined schema exactly, the ingestion process will fail. This configuration is critical for maintaining high data quality and preventing 'schema drift' in downstream analytical tables, ensuring that automated processes do not break due to unexpected changes in the upstream data structure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The extra column is automatically dropped.
Why it's wrong here
Dropping columns automatically is characteristic of 'flexible' or 'permissive' schema mapping. The 'strict' validation flag explicitly forbids this behavior to ensure that the data landing in the sink is exactly as defined, preventing potential data loss or ingestion of unexpected fields into the analytical model.
- ✗
The extra column is added to the destination schema.
Why it's wrong here
Adding columns dynamically is a feature of 'schema evolution' or 'permissive' modes. Under 'strict' configuration, the Dataflow will not alter the target table schema. It will reject the incoming data packet because it violates the predefined contract between the source and the sink structure.
- ✓
The ingestion process fails.
Why this is correct
The 'strict' schema validation setting requires the input data schema to be an exact match with the target sink. If an additional column is detected, the validation check fails, and the pipeline stops execution to prevent loading malformed or unexpected data into the Lakehouse storage.
- ✗
The extra column is logged as a warning.
Why it's wrong here
Dataflow Gen2 strict validation does not default to logging warnings and continuing. It is designed to be a blocking operation to ensure compliance with data governance policies. Simply logging a warning would bypass the integrity checks that the 'strict' mode is intended to enforce.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-700 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-700 exam.