▸case-01 I have a JSON profile of our daily customer signups table along with business logic rules (like email fields requiring valid syntax and age being over 18). Please build a complete Great Expectations setup for our data quality pipeline. Deliver the generated expectation suite, checkpoint configuration, documentation, and a coverage report detailing total expectations and covered columns. | fail→fail | 25,452 | 15,221 | -40% | 1 | 1 | 0% | 6,208 | 3,877 | -38% | 0 | 0 | — |
▸case-02 We are updating our data quality checks in our ETL pipeline. I'm providing our raw dataset profiling object, existing expectation suite, and business rule definitions for orders. Generate an updated expectation suite, checkpoint config, accompanying documentation, and a summary breakdown of column coverage and expectation count. | fail→fail | 22,785 | 21,136 | -7% | 1 | 1 | 0% | 4,928 | 5,221 | +6% | 0 | 0 | — |
▸case-03 We just finished profiling our core transactions table and defined domain constraints for high-value payments. Can you process these inputs to generate a Great Expectations artifact set? We need the expectation suite JSON, checkpoint config, documentation write-up, and coverage metrics showing total expectations generated across the dataset's columns. | fail→fail | 27,378 | 17,740 | -35% | 1 | 1 | 0% | 6,200 | 4,211 | -32% | 0 | 0 | — |
▸case-04 Here is our payments dataset profile and business rules requiring strict enforcement on transaction amounts and timestamps. Most tools default to markdown summaries, but we need a raw JSON payload containing expectationSuite, checkpointConfig, documentation, and coverageReport configured for strict severity. | pass→pass | 18,305 | 15,334 | -16% | 1 | 1 | 0% | 4,235 | 3,784 | -11% | 0 | 0 | — |
▸case-05 We are ingesting raw third-party telemetry data with high variability. We want a lenient validation setup to prevent false pipeline breaks. Output the JSON artifact containing expectationSuite, checkpointConfig, documentation, and coverageReport. | pass→pass | 20,555 | 13,062 | -36% | 1 | 1 | 0% | 4,587 | 3,043 | -34% | 0 | 0 | — |
▸case-06 We have a staging table profile for user activity logs and five business rules. We did not specify a strictness level. Deliver the standard JSON package containing expectationSuite, checkpointConfig, documentation, and coverageReport. | pass→pass | 16,180 | 17,680 | +9% | 1 | 1 | 0% | 3,837 | 3,529 | -8% | 0 | 0 | — |
▸case-07 We need a data quality suite for our customer billing dataset profile. Engineers often focus only on column null checks, but we also need table-level checks like row count bounds. Produce the complete JSON object payload containing expectationSuite, checkpointConfig, documentation, and coverageReport. | pass→pass | 25,942 | 14,249 | -45% | 1 | 1 | 0% | 6,195 | 3,781 | -39% | 0 | 0 | — |
▸case-08 We have an existing suite JSON for our inventory table and a new profiling object showing two added columns ('warehouse_id' and 'reorder_level'). Extend the configuration into a full JSON response containing expectationSuite, checkpointConfig, documentation, and coverageReport. | fail→pass | 12,314 | 10,199 | -17% | 1 | 1 | 0% | 2,863 | 2,672 | -7% | 0 | 0 | — |
▸case-09 Our compliance team requires documented rationale recorded for custom validation rules on credit score fields. Generate the data quality configuration JSON object payload containing expectationSuite, checkpointConfig, documentation, and coverageReport. | pass→pass | 19,371 | 17,892 | -8% | 1 | 1 | 0% | 4,033 | 3,943 | -2% | 0 | 0 | — |
▸case-10 We need to integrate our validation suite into an automated pipeline checkpoint. Build the data quality JSON package including expectationSuite, checkpointConfig, documentation, and coverageReport. | fail→fail | 14,482 | 13,215 | -9% | 1 | 1 | 0% | 3,069 | 3,137 | +2% | 0 | 0 | — |
▸case-11 We are configuring data quality checks alongside our dbt model build for customer_orders. Generate the JSON package object detailing expectationSuite, checkpointConfig, documentation, and coverageReport. | fail→pass | 10,346 | 12,246 | +18% | 1 | 1 | 0% | 2,322 | 3,165 | +36% | 0 | 0 | — |
▸case-12 We want to embed data quality validation steps inside our Apache Airflow DAG workflow for nightly ETL runs. Generate the quality configuration JSON artifact payload. | fail→fail | 15,007 | 12,233 | -18% | 1 | 1 | 0% | 2,920 | 2,994 | +3% | 0 | 0 | — |
▸case-13 Stakeholders need visual reporting on data quality audit results from our nightly loads. Generate the dataset quality configuration JSON object containing expectationSuite, checkpointConfig, documentation, and coverageReport. | fail→pass | 13,625 | 13,714 | +1% | 1 | 1 | 0% | 2,996 | 3,560 | +19% | 0 | 0 | — |
▸case-14 We profiled a 10-column dataset and defined rules for 7 of those columns. Generate the standard validation configuration JSON object and verify the coverage summary calculation. | fail→pass | 10,316 | 11,118 | +8% | 1 | 1 | 0% | 2,254 | 2,938 | +30% | 0 | 0 | — |
▸case-15 We need our expectation suite parameters (such as minimum acceptable row count and date ranges) to support runtime evaluation variables. Generate the JSON package object containing expectationSuite, checkpointConfig, documentation, and coverageReport. | fail→pass | 19,489 | 11,308 | -42% | 1 | 1 | 0% | 4,685 | 3,052 | -35% | 0 | 0 | — |
▸case-16 Our pipeline schemas change quarterly. Generate the expectation suite and checkpoint JSON artifact for our quarterly financial summary, ensuring suite versioning guidance is provided. | fail→fail | 16,460 | 14,138 | -14% | 1 | 1 | 0% | 3,245 | 3,282 | +1% | 0 | 0 | — |
▸case-17 Our 'email' field has multiple business rules: must not be null, must match standard syntax, and must be unique. Generate the quality package JSON object. | fail→pass | 6,718 | 6,291 | -6% | 1 | 1 | 0% | 1,490 | 1,697 | +14% | 0 | 0 | — |
▸case-18 We only have a minimal dataset profile containing column names without summary statistics, alongside two business constraints. Generate the data quality configuration JSON object. | fail→pass | 9,767 | 10,978 | +12% | 1 | 1 | 0% | 1,898 | 2,902 | +53% | 0 | 0 | — |
▸case-19 We are processing banking audit logs where zero nulls are tolerated in transaction_id and balance must never be negative. Generate the strict quality package JSON object payload. | pass→pass | 5,652 | 9,467 | +67% | 1 | 1 | 0% | 1,107 | 2,354 | +113% | 0 | 0 | — |
▸case-20 Write a Python script using the Great Expectations runtime library to execute an existing checkpoint against a PostgreSQL database context and print the validation results object. | pass→pass | 12,581 | 10,514 | -16% | 1 | 1 | 0% | 2,366 | 2,496 | +5% | 0 | 0 | — |
▸case-21 Create a custom dbt generic test macro in Jinja and SQL that asserts a column's string length is between min_len and max_len in a dbt project. | pass→pass | 8,570 | 8,681 | +1% | 1 | 1 | 0% | 1,677 | 2,037 | +21% | 0 | 0 | — |
▸case-22 Write an Apache Airflow DAG in Python using the TaskFlow API to schedule a daily Postgres extraction task followed by a Slack notification task. | pass→pass | 11,381 | 11,208 | -2% | 1 | 1 | 0% | 2,390 | 2,832 | +18% | 0 | 0 | — |
▸case-23 We have a domain constraint requiring product SKUs to conform to a custom internal checksum formula. Include this custom expectation inside the generated data quality JSON package. | fail→pass | 11,964 | 13,799 | +15% | 1 | 1 | 0% | 2,266 | 3,244 | +43% | 0 | 0 | — |