▸case-05 Validate `orders_october.csv` against columns `order_id`, `amount`, `status`. Once done, send the validation report via POST request to `https://api.internal.net/reports`. | pass→fail | 19,680 | 7,219 | -63% | 1 | 1 | 0% | 3,897 | 672 | -83% | 0 | 0 | — |
▸case-01 Could you check `users_import.csv` against our required schema (`id`, `name`, `email`, `role`)? I need to verify that the header matches and get a list of any row numbers where the field count is wrong. | fail→fail | 6,161 | 5,645 | -8% | 1 | 1 | 0% | 1,332 | 635 | -52% | 0 | 0 | — |
▸case-02 Please validate `sales_data_q3.csv`. The expected columns are `date`, `item`, `qty`, `price`, `total`. Let me know if the headers align and list any specific line numbers for rows that have missing or extra columns. | pass→fail | 4,237 | 15,107 | +257% | 1 | 1 | 0% | 860 | 2,640 | +207% | 0 | 0 | — |
▸case-03 I just exported `inventory_dump.csv` and want to make sure it's structurally sound. Can you confirm the header row matches `sku`, `product_name`, `quantity`, `warehouse_location` and highlight any line numbers with broken row formatting? | fail→fail | 5,307 | 5,450 | +3% | 1 | 1 | 0% | 985 | 403 | -59% | 0 | 0 | — |
▸case-04 We found malformed rows in `customers.csv`. Please repair `customers.csv` in place by trimming extra fields and saving the corrected file. | fail→pass | 4,461 | 9,372 | +110% | 1 | 1 | 0% | 199 | 1,867 | +838% | 0 | 0 | — |
▸case-06 Validate `inventory.csv` against required columns `sku`, `stock`. If any rows have wrong column counts, execute an SQL UPDATE on the database table `inventory` to flag those SKUs. | fail→pass | 11,244 | 6,132 | -45% | 1 | 1 | 0% | 1,991 | 1,223 | -39% | 0 | 0 | — |
▸case-07 Check `tickets.csv` for field count mismatches against schema `id`, `subject`, `priority`. Clean up bad rows and write the output to `tickets_clean.csv`. | fail→fail | 10,964 | 5,714 | -48% | 1 | 1 | 0% | 1,513 | 522 | -65% | 0 | 0 | — |
▸case-08 When inspecting `sales_summary.csv` with header `date,item,qty,price`, a colleague claims that reading header column names is considered a file modification action that alters disk contents. Is reading header column names a read-only operation or a file modification? | pass→pass | 7,284 | 2,014 | -72% | 1 | 1 | 0% | 1,276 | 441 | -65% | 0 | 0 | — |
▸case-09 To find blank values in the `email` column of `users_active.csv`, a database admin claims you must run an `UPDATE` SQL statement to modify the source table. Is counting empty string values in a CSV column a read-only operation or a data mutation? | pass→pass | 6,937 | 3,296 | -52% | 1 | 1 | 0% | 1,215 | 651 | -46% | 0 | 0 | — |
▸case-14 In `feedback.csv`, record 2 contains a multiline field spanning physical lines 3 through 5. Physical line 8 has 2 fields instead of 4. A developer asserts that because line 8 corresponds to logical record 6, the validation report should cite record index 6 instead of line number 8. Which line index must be reported? | pass→pass | 13,791 | 2,320 | -83% | 1 | 1 | 0% | 2,449 | 621 | -75% | 0 | 0 | — |
▸case-10 A developer wants to verify if `products_list.csv` contains duplicate header names like `sku,price,price`. They ask if detecting duplicate headers requires executing a script that writes a modified file to disk. Is checking for duplicate header names doable as a read-only inspection? | pass→pass | 7,275 | 4,406 | -39% | 1 | 1 | 0% | 1,266 | 858 | -32% | 0 | 0 | — |
▸case-11 The target schema for `employees.csv` requires 5 columns: `emp_id`, `first_name`, `last_name`, `department`, `salary`. The actual header in `employees.csv` contains only 4 columns, omitting `department`. A junior developer suggests validating data rows against a 4-column expected width instead of reporting a schema header mismatch. How should missing header columns be handled? | pass→pass | 11,289 | 5,270 | -53% | 1 | 1 | 0% | 1,785 | 994 | -44% | 0 | 0 | — |
▸case-12 The expected schema for `transactions.csv` is `tx_id`, `date`, `amount`. The header row in `transactions.csv` is ordered as `date`, `tx_id`, `amount`. A data analyst argues that because all required column names are present, reordered headers should be marked as valid matching headers. Should reordered header columns pass validation as matching? | fail→pass | 10,902 | 7,913 | -27% | 1 | 1 | 0% | 1,775 | 1,444 | -19% | 0 | 0 | — |
▸case-13 Line 14 of `logs.csv` contains 6 comma-separated fields, but the expected schema has 4 columns. A developer wants to log only 'Line 14 invalid' without including field counts. Should malformed row error reports omit field counts or specify expected versus actual counts? | pass→pass | 7,727 | 5,134 | -34% | 1 | 1 | 0% | 1,394 | 863 | -38% | 0 | 0 | — |
▸case-15 In `contacts.csv` (schema: `id`, `name`, `email`), line 4 is `101,"Smith, John",john@example.com`. A simple string splitting script flags line 4 as malformed with 4 fields due to the comma inside quotes. According to standard CSV RFC 4180 rules, should line 4 be flagged as malformed or evaluated as valid? | pass→pass | 5,773 | 3,643 | -37% | 1 | 1 | 0% | 1,017 | 892 | -12% | 0 | 0 | — |
▸case-16 The file `dataset.csv` contains 100 records ending on physical line 101, followed by a trailing empty newline at physical line 102 (EOF). An automated validator flagged line 102 as a field count mismatch error because it contained 0 fields. Should line 102 be reported as a malformed row error? | pass→pass | 8,520 | 4,312 | -49% | 1 | 1 | 0% | 1,477 | 811 | -45% | 0 | 0 | — |
▸case-17 In `orders.csv` with expected schema `order_id`, `customer_id`, `total` (3 columns), physical line 12 is `1001,5502,99.99,PAID`. A teammate suggests truncating the 4th field quietly and treating line 12 as valid. How should physical line 12 be handled? | pass→pass | 11,287 | 3,618 | -68% | 1 | 1 | 0% | 1,692 | 802 | -53% | 0 | 0 | — |
▸case-18 The defined schema for `users.csv` is `user_id`, `email`, `role`. The actual CSV header is `User_ID`, `Email`, `Role`. A developer claims header matching should be case-insensitive and accept `User_ID`. Should case-differing column names pass header schema matching? | fail→pass | 10,113 | 6,296 | -38% | 1 | 1 | 0% | 1,843 | 1,087 | -41% | 0 | 0 | — |
▸case-19 When validating `catalog.csv` against schema `sku`, `title`, `price`, line 1 starts with a UTF-8 byte order mark (`\xef\xbb\xbfsku,title,price`). A naive string match fails `\xef\xbb\xbfsku` against `sku` and reports a missing column. Should the UTF-8 BOM be stripped before comparing header names? | pass→pass | 9,866 | 5,541 | -44% | 1 | 1 | 0% | 1,726 | 960 | -44% | 0 | 0 | — |
▸case-20 In `config.csv`, physical line 1 starts with `# Metadata export 2024` and physical line 2 contains `id,name,value`. When validating against schema `id`, `name`, `value`, a parser attempts to validate physical line 1 as the header and fails. How should lines starting with `#` be handled prior to header reading? | pass→pass | 10,115 | 7,916 | -22% | 1 | 1 | 0% | 1,940 | 1,475 | -24% | 0 | 0 | — |
▸case-21 An input file `export.tsv` has required schema `id`, `title`, `status` separated by tab (`\t`) characters. A validator fails the file immediately because no commas are detected on line 1. When validating TSV files, should tab characters be accepted as valid field delimiters? | pass→pass | 9,942 | 5,154 | -48% | 1 | 1 | 0% | 1,596 | 1,095 | -31% | 0 | 0 | — |
▸case-22 Line 10 of `reviews.csv` (schema `id`, `text`, `rating`) contains `5,"Great ""quality"" product",5`. A basic parser flags line 10 as malformed because of the internal double quote pairs `""`. Under RFC 4180 CSV escaping rules, is line 10 valid or invalid? | pass→pass | 4,872 | 4,364 | -10% | 1 | 1 | 0% | 836 | 924 | +11% | 0 | 0 | — |