DP-700 Ingest and Transform Data Practice Question
Exhibit
{
"source": "RawCSV",
"format": "CSV",
"encoding": "UTF-8",
"column_delimiter": ",",
"first_row_as_header": true
}Refer to the exhibit. You are loading this file into a Lakehouse. You notice that the column headers contain special characters, and the data is failing to load correctly. What should you do?
⚠ Common exam trap
Candidates often try to rename columns within the source file manually, which is inefficient, instead of using Fabric's built-in schema mapping to handle invalid characters during ingestion.
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
✓
Disable the 'first_row_as_header' setting and use custom schema mapping
When dealing with CSV files, column headers must be compatible with the destination system's naming conventions. Often, special characters in source headers are invalid for column names in the target Lakehouse tables. By sanitizing headers or choosing to skip them and using custom column names, you ensure the load completes successfully. This is a common real-world challenge when integrating data from disparate source systems into a unified analytical Lakehouse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the file encoding to ASCII
Why it's wrong here
ASCII encoding would likely strip or corrupt the special characters rather than fixing the underlying issue with column naming. It does not address the fundamental conflict between the source header content and the naming restrictions required by the target table schema.
- ✓
Disable the 'first_row_as_header' setting and use custom schema mapping
Why this is correct
By disabling the header auto-detection, you can explicitly define the mapping in the Copy activity. This allows you to map the data from the CSV into properly named, valid columns in the target table, bypassing the invalid characters present in the raw source file headers.
- ✗
Increase the pipeline retry count
Why it's wrong here
Increasing the retry count will not solve a data schema or naming conflict. The pipeline will fail on every attempt because the fundamental validation of the column name remains invalid. Retries are for transient environmental errors, not for structural issues with the source data.
- ✗
Convert the CSV to binary format
Why it's wrong here
Converting to binary does not resolve the structural issue. The issue is about how the column names are parsed and mapped to the target schema. Binary data would still require parsing of the header information, which would continue to fail because the characters remain fundamentally invalid.
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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.