Input Data Testing for Machine Learning Systems

CT-AI · 45 questions

• A rec-center scholarship screener treats one age band differently after being fitted on a historically skewed attendance pile. Which mitigation should the tester pick?
• A sidewalk-heave project bought a street-condition dump from a reseller with no chain of custody, and the files sat on an open share. Which mitigation should the tester pick?
• A water-meter anomaly pile includes a cluster of rows that look planted. Which official mitigation family should the tester name without teaching later techniques?
• A bus-stop board shows passenger counts below zero, mixed units in one column, and the word “busy” in a count field. Which mitigation should the tester pick?
• A food-bank forecast kept a locker-number column that never moves with demand. Which mitigation should the tester pick?
• A recycling-sort pile over-represents bottles, was padded with synthetic cans, has empty nights, covers only weekday routes, and never saw the high end of contamination. Which mitigation should the tester pick?
• Pest photos were tagged with vague rules and disagreeing annotators; the live pipe also drops night frames and has no check for uncontrolled edits. How should those two defects be split?
• A community-solar grant screener systematically scores one neighborhood lower. How should a tester define MLS bias here?
• A tool-library late-fee helper either inherited a decade of uneven write-offs or applies a single late-day cut that hits one age band. How should those two sources be told apart?
• Before anyone plots a chart, a tester is asked how to hunt bias introduction. Where should that hunt start?
• A rec-center dataset folder has notes on how attendance was collected, who tagged it, and which neighborhoods appear. Which bias-test approach uses that folder?
• Prep scripts quietly drop a language-preference field, and the model code treats a blank as a default majority value. Which bias-test approach finds those anti-patterns?
• Exploratory plots and clusters of a transit late-bus pile show one depot’s night shifts bunched away from the rest. What should that reveal to a tester?
• Testers send a known unbiased, representative booking set through a clinic no-show model and look for statistically significant outcome gaps across groups. Which bias test is that?
• A rec-center scholarship model is checked by identifying sensitive attributes, building plausible counterfactual rows, scoring them, and looking for a statistically meaningful flip — including combinations of attributes. What must the tester also refuse?
• A water-meter anomaly project is still in design. How should pipeline testing start?
• A pool-chemistry pipe has an ingest job, a transform script, and a sensor interface. Which testing covers those pieces?
• Testers of a grain-moisture pipe must show validation rules fire, bad rows are handled, and there is no easy hole for planted or hostile files. Which pipeline tests are those?
• Cleaned moisture rows leave stage one as “pct” and stage two still reads “percent.” What kind of defect is that?
• The assembled sidewalk-heave pipe is checked with a smoke pass, functional transform and routing cases, a load, scale, and security pass, and deliberately defective frames to see whether integrity holds. Which system-test set is that?
• Operations rebuilt the live pest-photo pipe. Testers want to know it still does the same job as the training pipe. Which check should they name?
• A solar-output pipe talks to a bureau feed, a store, a monitor, and the model; a new transform is tried beside the old one on the live stream; nobody is sure which code and which file set are in each place. What three pipeline checks should the tester combine?
• The training pipe for a bus-late model was a messy prototype aimed at keeping rows honest. The night-shift operational pipe must stay up, fast, and maintainable. What should drive test emphasis?
• A sidewalk-heave team fitted on last winter’s shed photos. Spring heave on wet brick is the live job. How should representativeness be defined?
• Before comparing piles, testers of a rec-center no-show model spell out use cases, users, sites, expected mixes, and hard edge days — using desk experts, an older booking system, and a trusted public benchmark — then take a stratified sample so every subgroup is in the baseline. Which representativeness step is that?
• Testers plot the candidate fitting set and a reference operational set side by side (histograms, scatter) and check that feature pairings that matter in the yard still appear. What should they use?
• A recycling pile has a dense clump of clear bottles, no night bags, and almost no multi-material class. What representativeness findings are those?
• A solar-output team wants more than a looks-different comment when they compare the fitting mix with a reference operational mix. Which representativeness step should the tester name?
• A dairy project wants to fit this afternoon and check the pile later if the score is poor. When should representativeness testing be done?
• Once a flood-stage model is live, incoming sensor mixes start to wander from the fitting year. What should the tester continue, without teaching a Chapter 6 method?
• A grain-silo fitting set has empty moisture cells and a few rows with the moisture attribute missing entirely. Which dataset constraint should the tester apply?
• Public-pool logs include a turbidity of -4 on a scale that is only defined from 0 to 40. Which dataset constraint should the tester apply?
• A bus-stop board expects an integer passenger count and received the word “crowded.” Which dataset constraint should the tester apply?
• A rec-league sportsmanship file lists point awards whose daily total is above the board’s stated cap, and some days sit under the stated floor. Which dataset constraint should the tester apply?
• After dropping null night readings, a water-meter set has fewer non-null rows than the operations rule requires for a valid day. Which dataset constraint should the tester apply?
• A water-utility dump contains two identical meter rows and a third that differs by a single transposed digit the rule treats as a near duplicate. Policy allows zero. Which dataset constraint should the tester apply?
• A permit-triage table includes a ticket-number column in which every value is unique, like a raw identifier. Which dataset constraint should the tester apply?
• A tool-library log shows a return timestamp earlier than the checkout, hours-open that do not move with recorded energy use, and one temperature no sensor in the shed has ever produced. Which dataset constraints should the tester apply, and where do they run?
• A supervised orchard-pest identifier was fitted on photos whose tags disagree with the agronomist. Why does label quality matter?
• A trained archivist reviews a sample of handwriting tags using the desk’s labelling guide. Which label-correctness approach is that?
• The same wildlife clip is tagged independently by two people and compared. Agreement is scored (percentage or a chance-corrected coefficient). A low score points to a weak guide, an ambiguous clip, or poor tagging — not automatically to a bad model. Which method is that?
• A recycling project cannot re-tag everything. Testers send near-boundary bags and the contamination class that would close the line to a second pass. How should the review budget be spent?
• A second depot’s late-bus set has a well-trusted tag mix. The new depot’s mix is oddly short on weather-hold. Which label-correctness approach flags that?
• Audio event tags on rink-plant recordings sometimes have a start later than the end, or a span that leaves the clip. Which label-correctness approach should the tester pick?
• During fitting, some pest photos keep a high loss against their tags; a later pass also marks low-confidence items. How should those signals be used?