When handling erroneous data in Snowflake, what approaches can be employed to effectively address data anomalies and ensure data integrity? (Select all that apply)

Question

Options

• ADropping all erroneous records
• BUsing error tables to capture and analyze erroneous data
• CPerforming data transformations without error handling
• DUtilizing clones for isolated error resolution

• A
  15% (8)
• B
  80% (44)
• C
  5% (3)

Explanation

Error tables (B) allow you to capture rejected or malformed records during data loading (e.g., via COPY INTO with ON_ERROR = CONTINUE), preserving them for analysis and correction rather than losing them silently. Clones (D) let you create zero-copy copies of tables or schemas, giving you an isolated sandbox to test fixes and transformations without risking production data integrity.

Option A is wrong because blindly dropping erroneous records destroys potentially recoverable data and is rarely an acceptable production strategy. Option C is wrong because performing transformations without error handling is the opposite of ensuring data integrity - it lets bad data propagate silently downstream.

Memory tip: Think "Capture and Contain" - B captures errors (error tables), D contains the risk (clones). Both preserve your ability to recover; A destroys data and C ignores the problem entirely.

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