DEA-C02 Data Movement Practice Question
A Data Engineer is using the COPY INTO <table_name> command to load Parquet files. The source files contain new columns that do not yet exist in the target Snowflake table. Which TWO features or settings should be used to handle this automatically? (Select TWO)
⚠ Common exam trap
Candidates often select only the schema evolution setting and forget that the COPY INTO command must also be explicitly told how to map columns using the match-by-name parameter.
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
✓
Set the table property ENABLE_SCHEMA_EVOLUTION = TRUE.
Snowflake provides schema evolution capabilities to simplify the ingestion of evolving datasets. By enabling the ENABLE_SCHEMA_EVOLUTION property on the table, Snowflake allows the COPY command to modify the table structure. When combined with MATCH_BY_COLUMN_NAME, Snowflake maps the Parquet fields to table columns and automatically adds any missing columns found in the source files to the target table.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the table property ENABLE_SCHEMA_EVOLUTION = TRUE.
Why this is correct
This table-level property is essential for allowing DML operations like COPY to automatically perform DDL changes. Without this setting, even if the COPY command identifies new columns, it will fail or ignore them because it lacks the authorization to alter the underlying table schema during the data loading process.
- ✓
Use the MATCH_BY_COLUMN_NAME = CASE_SENSITIVE option in the COPY command.
Why this is correct
When loading semi-structured data like Parquet, this option tells Snowflake to match the fields in the file to the columns in the table by name rather than position. In conjunction with schema evolution, it allows Snowflake to detect new fields in the Parquet file and create corresponding columns in the table.
- ✗
Use the STRIP_OUTER_ARRAY = TRUE file format option to flatten the Parquet data.
Why it's wrong here
The STRIP_OUTER_ARRAY option is specific to JSON files where the root element is an array. Parquet is a columnar format and does not use this specific structure. More importantly, this option assists with parsing the file structure but does not contribute to the automatic evolution of the table schema.
- ✗
Set ON_ERROR = 'CONTINUE' to ensure the command doesn't fail when it sees new columns.
Why it's wrong here
The ON_ERROR = 'CONTINUE' setting tells Snowflake to ignore rows that have errors and keep loading others. It does not enable the creation of new columns. If schema evolution is not enabled, the COPY command will simply ignore the extra data in the new columns or fail depending on other settings.
- ✗
Apply a UDF in the COPY statement to dynamically cast the Parquet schema to the table.
Why it's wrong here
While transformations are supported in COPY, they require the engineer to explicitly define the mapping for every column. This approach is manual and does not provide an 'automatic' way to handle new, unknown columns that might appear in the source files as the data schema evolves over time.
About these practice questions
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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 Snowflake exam blueprint
This DEA-C02 practice question is part of Courseiva's free Snowflake 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 DEA-C02 exam.