If you receive no errors when running your workspace, the output must be correct.

If you receive no errors when running your workspace, the output must be correct.

• A. True
• B. False
• C. Sometimes
• D. Depends on data

Answer: (B)

No errors during a run only means the workspace executed without runtime failures; it doesn’t prove the results are correct. Output can be wrong even when everything runs cleanly because correctness depends on data, transformation logic, and configuration, not just on whether the engine reports errors. You could have miswired a transformer, used an incorrect attribute mapping, or applied a formula that returns a value after all syntactic checks pass but isn’t the value you expect. If input data lacks the fields you assume, transforms might fill in defaults or nulls rather than flag a problem, and the resulting dataset can look valid even though the values are not what you need. A coordinate system mismatch can produce geometries that are perfectly valid in their CRS but incorrect for the target system. Filtering conditions might be too permissive or too restrictive, letting through the wrong features. Joins can look fine structurally but combine records in ways that don’t reflect the real relationships you intended, yielding a dataset that’s technically valid yet semantically wrong. Because of these possibilities, validating outputs with checks, sample inspections, and comparison against expected results is essential. Use test data, count features, verify attribute values, and perform domain-specific checks to confirm correctness beyond the absence of errors.

No errors during a run only means the workspace executed without runtime failures; it doesn’t prove the results are correct. Output can be wrong even when everything runs cleanly because correctness depends on data, transformation logic, and configuration, not just on whether the engine reports errors.

You could have miswired a transformer, used an incorrect attribute mapping, or applied a formula that returns a value after all syntactic checks pass but isn’t the value you expect. If input data lacks the fields you assume, transforms might fill in defaults or nulls rather than flag a problem, and the resulting dataset can look valid even though the values are not what you need. A coordinate system mismatch can produce geometries that are perfectly valid in their CRS but incorrect for the target system. Filtering conditions might be too permissive or too restrictive, letting through the wrong features. Joins can look fine structurally but combine records in ways that don’t reflect the real relationships you intended, yielding a dataset that’s technically valid yet semantically wrong.

Because of these possibilities, validating outputs with checks, sample inspections, and comparison against expected results is essential. Use test data, count features, verify attribute values, and perform domain-specific checks to confirm correctness beyond the absence of errors.