▸case-01 We are setting up automated data testing for our Snowflake data warehouse tables. Our junior engineer suggested only checking row counts every morning. What structural validation check must be included alongside volume checks to catch upstream column type changes or missing fields? | fail→fail | 9,386 | 25,393 | +171% | 1 | 1 | 0% | 1,515 | 1,609 | +6% | 0 | 0 | — |
▸case-02 Our executive dashboard recently displayed stale data for 3 days because pipeline runs failed silently without throwing errors. The data structure was intact. What specific quality test dimension addresses how up-to-date the data is? | fail→fail | 5,392 | 4,962 | -8% | 1 | 1 | 0% | 893 | 1,052 | +18% | 0 | 0 | — |
▸case-03 In our core customer dimension table, duplicate primary keys were generated due to an unnested JSON array in an upstream ETL job. Which fundamental data test constraint prevents primary key duplication? | pass→pass | 5,060 | 4,611 | -9% | 1 | 1 | 0% | 844 | 990 | +17% | 0 | 0 | — |
▸case-04 When joining our orders table with the users table, hundreds of orders are failing to map because foreign keys reference nonexistent user IDs. What data test type catches broken foreign key relationships? | pass→pass | 5,949 | 4,733 | -20% | 1 | 1 | 0% | 1,152 | 1,067 | -7% | 0 | 0 | — |
▸case-05 Our checkout analytics pipeline received 'PENDING_PAYMENT' as a new order status, but downstream reporting models broke because they only expected 'SUCCESS' or 'FAILED'. What categorical data quality test prevents unexpected enum values from passing silently? | pass→pass | 8,239 | 6,990 | -15% | 1 | 1 | 0% | 1,521 | 1,482 | -3% | 0 | 0 | — |
▸case-06 A source API change caused our daily ingestion process to pull only 5 rows instead of the usual 500,000, but because no fields were null, schema tests passed. What data validation dimension detects abnormal drop-offs in record counts? | fail→pass | 6,612 | 4,186 | -37% | 1 | 1 | 0% | 1,133 | 959 | -15% | 0 | 0 | — |
▸case-07 Our data team gets pinged on PagerDuty every time a minor non-critical staging table has a 1-minute freshness delay, leading engineers to ignore all PagerDuty alerts. How should alert routing be structured to differentiate critical business incidents from low-priority warnings? | fail→pass | 15,888 | 13,840 | -13% | 1 | 1 | 0% | 2,776 | 2,473 | -11% | 0 | 0 | — |
▸case-08 Currently, all data pipeline errors write to a log file on an EC2 instance that nobody checks until business users complain about broken reports. What alerting component ensures notification reaching active team communication spaces? | pass→pass | 8,710 | 8,737 | +0% | 1 | 1 | 0% | 1,510 | 1,690 | +12% | 0 | 0 | — |
▸case-09 When a data outage occurs on Saturday night, alerts are emailed to the lead data engineer who is frequently off-grid, leaving alerts unhandled until Monday. What operational staffing mechanism ensures 24/7 accountability for incoming data alerts? | pass→pass | 8,732 | 7,333 | -16% | 1 | 1 | 0% | 1,485 | 1,434 | -3% | 0 | 0 | — |
▸case-10 An unacknowledged critical data alert has been sitting in Slack for 4 hours because the primary on-call engineer went into a meeting. What process automatically re-routes unacknowledged alerts to secondary engineers or engineering managers? | fail→pass | 7,318 | 9,295 | +27% | 1 | 1 | 0% | 1,325 | 1,864 | +41% | 0 | 0 | — |
▸case-11 When a critical dbt model build fails during morning processing, engineers handle the issue randomly without standard initial assessment steps. What structured artifact provides standard immediate steps for identifying scope and severity upon incident onset? | pass→pass | 7,873 | 7,707 | -2% | 1 | 1 | 0% | 1,236 | 1,512 | +22% | 0 | 0 | — |
▸case-12 During a data outage, engineers spend 45 minutes drafting custom Slack messages to stakeholders, causing delays and inconsistent status updates. What tool streamlines status reporting to non-technical stakeholders during an incident? | pass→pass | 10,777 | 9,208 | -15% | 1 | 1 | 0% | 1,718 | 1,750 | +2% | 0 | 0 | — |
▸case-13 Data incidents often linger for days because there is no clear time-bound benchmark for when an incident must be fixed based on its severity level. What metrics specify acceptable recovery times for data issues? | pass→pass | 12,523 | 12,471 | -0% | 1 | 1 | 0% | 2,221 | 2,392 | +8% | 0 | 0 | — |
▸case-14 A junior analyst modified a production SQL transformation directly in the warehouse, breaking revenue reports for the CFO. What change management process ensures peer review before modifications hit production data models? | pass→pass | 11,303 | 12,315 | +9% | 1 | 1 | 0% | 1,984 | 2,355 | +19% | 0 | 0 | — |
▸case-15 A deployment of new data transformations introduced corrupted calculations across 50 tables, and the team had to spend 12 hours manually rewriting code to undo the change. What mandatory component of a deployment request allows fast reversion to previous stable states? | fail→fail | 3,274 | 3,790 | +16% | 1 | 1 | 0% | 503 | 839 | +67% | 0 | 0 | — |
▸case-16 Someone dropped a production data schema yesterday, but nobody knows who performed the action or from which service account. What operational mechanism tracks schema modifications and pipeline code changes? | fail→fail | 12,565 | 12,808 | +2% | 1 | 1 | 0% | 2,112 | 2,416 | +14% | 0 | 0 | — |
▸case-17 After fixing a data pipeline outage, the team simply closes the ticket without investigating why the upstream API schema changed unexpectedly. What postmortem analysis technique identifies the underlying systemic origin of the failure? | pass→pass | 8,558 | 8,084 | -6% | 1 | 1 | 0% | 1,407 | 1,578 | +12% | 0 | 0 | — |
▸case-18 We held a debrief meeting after a major data outage and identified three vulnerabilities, but 2 months later the exact same issue recurred because no follow-up work was tracked. What postmortem outcome prevents recurring failures by tracking preventative action items? | pass→pass | 9,027 | 8,175 | -9% | 1 | 1 | 0% | 1,478 | 1,549 | +5% | 0 | 0 | — |
▸case-19 A complex data pipeline incident was resolved by a senior engineer, but when a similar incident happened six months later while that engineer was on vacation, no one knew how to solve it. What step completes the postmortem workflow to preserve operational knowledge? | pass→pass | 6,827 | 5,259 | -23% | 1 | 1 | 0% | 1,062 | 1,112 | +5% | 0 | 0 | — |
▸case-20 Our data engineers run quality checks manually on their laptops before deploying data pipelines, but sometimes skip them due to tight deadlines, pushing bad code to production. Where should data quality gate checks be embedded to automatically prevent bad deployments? | fail→pass | 13,358 | 12,565 | -6% | 1 | 1 | 0% | 2,276 | 2,351 | +3% | 0 | 0 | — |
▸case-21 Our data platform sends 400 Slack alerts per day for minor volume fluctuations, causing the team to mute the channel and miss critical database crashes. What operational practice addresses this alert fatigue? | fail→pass | 11,598 | 9,269 | -20% | 1 | 1 | 0% | 1,891 | 1,689 | -11% | 0 | 0 | — |
▸case-22 Our Snowflake query runtime for the monthly sales aggregation table increased from 2 minutes to 45 minutes due to an unindexed massive cross join. How should we rewrite the SQL join logic to optimize query performance? | pass→pass | 15,429 | 14,041 | -9% | 1 | 1 | 0% | 2,704 | 2,711 | +0% | 0 | 0 | — |
▸case-23 We are streaming 50,000 events/sec into our data lake and need to size our Apache Kafka cluster brokers, partitions, and replication factors. What hardware specifications and partition strategies should we choose for this throughput? | pass→pass | 18,554 | 18,715 | +1% | 1 | 1 | 0% | 3,187 | 3,752 | +18% | 0 | 0 | — |
▸case-24 Our business users find our Tableau executive dashboard confusing because color palettes conflict and charts lack clear visual hierarchy. What design best practices should we apply to improve UI readability in Tableau? | fail→fail | 16,401 | 14,672 | -11% | 1 | 1 | 0% | 2,791 | 2,852 | +2% | 0 | 0 | — |