What troubleshooting step would you take if you see an incorrect join output without errors?

• A. Check the FeatureJoiner output port feature counts
• B. Review the data types in the schema
• C. Restart the translation
• D. Check the color of dataset icons

Answer: (A)

When diagnosing a join that looks incorrect but has no errors, the quickest and most informative check is the actual counts of features flowing through the joiner’s output ports. The FeatureJoiner can produce different numbers of features depending on how well the join keys match and on the join type you’ve configured. By looking at how many features appear on the joined output versus how many came in on each input, you can immediately see where the mismatch lies: more output than expected hints at duplicates or multiple matches; fewer or zero output hints at missing matches or misaligned keys. This diagnostic detail helps you decide whether you need to adjust the join keys, switch input roles (which side is considered left/right), or address duplicates before joining. Other options don’t directly reveal the join’s data relationships or cardinality, and restarting the translation or checking unrelated visuals won’t address the underlying join issue.