Repeatability
High
The operations — deduplication, category normalization, sentiment imputation — are structurally identical across all 3,200 records. This is a batch transformation job with no per-ticket unique judgment required.
Ambiguity Tolerance
High
Success criteria are concrete: 80 duplicates removed, category names standardized to a canonical list, missing sentiment filled in, output as CSV. There's minor ambiguity in how to handle novel category variants, but that's a small edge case, not a structural problem.
Data & Tool Availability
High
The user has the data exported and ready as JSON. A code agent can process it locally with pandas or similar, and a language model can handle sentiment inference inline. No external API access or live system permissions are needed.
Error Cost
Low
The original Zendesk data is preserved; the output is a new CSV. Any errors in sentiment labels or category mappings are easily audited and corrected before analysis. Nothing irreversible happens here.
Human Judgment Required
Low
Sentiment inference from ticket text is well within current LLM capability. Category consolidation ('billing' vs 'account-billing') is a straightforward normalization rule a human would define once and an agent applies consistently. A brief human review of the category mapping table is advisable but not strictly required.