CT-GenAI practice questions
ISTQB · CT-GenAI · 300 questions
Validates tester-level skill in applying large language models and generative AI across the software testing lifecycle, including prompt engineering, evaluating AI-generated testware, managing risks such as hallucinations and data privacy, and adopting LLM-powered test infrastructure and agents.
This course contains the use of artificial intelligence.
About the CT-GenAI exam
- Time allowed
- 1 hour
- Questions
- 40
- Passing score
- 30 of 46 points (65%)
- Format
- Delivered by accredited ISTQB exam providers; 25% extra time (75 minutes) for non-native language speakers
Exam details published by the vendor, checked 25 August 2026. Vendors change fees and formats without notice — confirm on the vendor's own page before you book.
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Introduction to Generative AI for Software Testing · 53 questions
- A municipal parking-permit desk encodes “resident + one vehicle + no unpaid tickets → approve” as if-then lines a clerk can open and edit. Which type of AI does this approach represent?
- A community-theater box office wants last season’s no-shows sorted into “likely late” versus “likely cancel” after a person picks columns such as weekday and weather. Which AI approach does this describe?
- A food-bank inventory app has thousands of photographed shelf labels, and nobody wants to hand-list traits such as font size or glare. Which type of AI is designed to learn features automatically from that kind of large, complex input?
- A harbor-ferry tester asks a pre-trained model to draft a weekend-sailing test script from a user story rather than to classify last week’s tickets. Which type of AI does this use?
- Four suppliers pitch a public-library hold system: a readable rule engine, a no-show scorer with hand-picked columns, a photo-spine reader that learned its own features, and a model that writes hold-cancellation notices. Which mapping of pitch to AI type is correct?
- A community-solar billing tester is told they must first collect a labeled training set before any AI may draft test cases. What Chapter 1 advantage of generative AI for testers corrects that claim?
- Last year’s defect tickets feed a predictor that only assigns categories such as “install” or “pay.” A second tool drafts new cases from this sprint’s stories. How should a tester classify these two uses?
- A youth-soccer registration tester hears “we bought generative AI” and assumes any image-painting model can review user stories. What correctly describes an LLM in this setting?
- A farmers-market stall-booking team can only host a compact on-laptop model for drafting reminder texts. How should they describe a small language model (SLM) compared with a general LLM?
- A municipal snow-route incident note is split into sub-words and punctuation before the model processes it. Which LLM basic does this describe?
- Two co-op grocery loyalty defect notes—“points vanished” and “balance dropped”—land near each other in a numeric space so the model treats them as related. Which LLM basic does this describe?
- A community-college LMS tester mixes up “how the story is chopped,” “how each piece is numbered for meaning,” and “how much of the log still fits.” Which statement keeps the three LLM basics correctly separated?
- A city bike-share tester asks why the model writes the next sentence of a defect report one piece at a time. What best explains transformer inference at tester depth?
- A credit-union mobile-deposit tester accepts a fluent expected-result paragraph that invents a weekend fee the specification never stated. What Chapter 1 limit does this illustrate?
- A volunteer-shift scheduler tester pastes the same user story twice and receives two different case lists. What Chapter 1 explanation fits best?
- A municipal recycling-pickup tester pastes three weeks of route logs and the model “forgets” Monday’s missed stops. What does the context window represent?
- A community-garden plot tester wants every historic booking CSV in one prompt “so nothing is missed.” What trade-off about a larger context window should they understand?
- A small-clinic appointment book is described as “powered by a generative pre-trained transformer.” What does that phrase refer to for testers?
- A school-lunch allergy team is quoted three weeks to “train a model on our menus” before anyone may ask for test cases. What practical tester benefit of pre-trained LLMs answers that delay?
- A harbor-slip reservation tester’s pasted API log is silently cut off mid-stack-trace. Why should testers care whether prompt plus history fit the context window?
- A municipal tree-trimming work-order dump is full of long hyphenated IDs that split into many tokens. What tester implication of tokenization does this illustrate?
- A community-radio pledge tester asks the model to rewrite a fuzzy donor story into clearer acceptance language. What should they understand about LLM language help?
- A city-pool lane-booking team is offered a general model trained on huge mixed text, code, and image data that still needs adaptation before it will emit their lane-hold case table. Which LLM category is that?
- A credit-union wire-transfer tester gets cleaner case drafts after switching to a model further trained on prompt–expected-response pairs so it obeys “list three boundary cases.” Which LLM category is that?
- A municipal poll-worker eligibility screen has nested if-then rules, and the tester needs a model that can walk those steps before proposing a case order. Which LLM category fits best over a plain instruction-tuned model?
- A food-truck permit tester needs an LLM for two jobs: (a) rewrite one user story into a simple case table, and (b) justify a risk-based case order from several competing constraints. Which pairing best matches model type to task?
- An interlibrary-loan tester pastes raw user stories into a foundation model and receives long essays instead of given-when-then cases. What best explains this outcome for test artefact generation?
- A municipal water-meter coach tells the team to “use the non-reasoning model for the boilerplate case list.” In this syllabus, which model type does that label refer to?
- A community-orchestra seat-map tester is told to “test the reasoning model with neuron coverage.” For CT-GenAI work, what is the appropriate focus?
- A municipal dog-license tester waits on a reasoning model only to change the word “owner” to “guardian” throughout a template case. What is the best assessment of that model choice?
- A bike-share kiosk team has a wireframe PNG plus a one-line user story and wants unlabeled controls called out for testing. Which model choice best fits the task?
- A bike-share screen shows the button label “Unlock” while the user story says the primary action is “Start ride.” How should a tester best use a vision-language (multimodal) model here?
- A clinic kiosk defect report says “the Confirm button is missing,” and the attached build screenshot still clearly shows Confirm. Which prompt approach best helps flag that mismatch?
- A garden-plot map wireframe shows a disabled “Waitlist” control that the user story never mentions. Why would a tester feed both the wireframe image and the story into a multimodal model when drafting cases?
- A pool-lane booking screenshot must inform a multimodal test prompt. A colleague only types lane.png into the chat. What is the key limitation of that approach?
- A radio-pledge tester has a well-written user story and no UI yet. A colleague insists they must attach a mock screenshot before any LLM help. What is the soundest response?
- A building-permit portal tester pastes a wireframe into a multimodal chat and writes only “what do you see?” Which change best aligns the prompt with a testing goal?
- A snow-route map image is low-contrast, and a multimodal model “sees” a button that the PNG does not contain. How should the tester treat that output on this exam?
- A ferry check-in story says “fast check-in for regulars” but never defines who counts as a “regular.” Which LLM use best matches requirements-analysis support?
- A library-hold user story is given to an LLM, which returns a draft set of cases and suggested test objectives. Which GenAI testing capability does this primarily illustrate?
- A community-solar story states a 3% credit. A tester asks an LLM for expected-result lines for three usage bands. What capability is this, and what constraint still applies?
- A soccer age-band registration form needs under-8, on-boundary, and over-max birthdates without using real children’s records. Which GenAI testing capability does requesting those values primarily illustrate?
- A stall-booking case list is given to an LLM, which returns a first-draft keyword script and suggests adding a boundary check on the date field. Which capability is illustrated?
- After a recycling-route regression, a tester pastes a noisy log into an LLM and asks for a short summary plus severity and priority buckets. Which capability does this represent?
- A garden-plot release is slipping, and the tester asks an LLM for an updated test-plan paragraph plus a defect-report skeleton. Which capability is this primarily?
- A clinic-booking lead says GenAI is “only for writing cases.” Which statement best reflects the official capability view?
- A new poll-worker tester types “what is a test condition on this story?” into a chat pane and iterates on the reply. Which interaction model does this describe?
- A water-meter suite auto-drafts cases on every build through an API hooked to the test tool; nobody is chatting. Which interaction model is this?
- A food-truck permit team must choose between nightly batch case refresh and ad-hoc “explain this requirement” help. How do the two GenAI interaction models compare for those needs?
- Stakeholders who do not write scripts want to explore a lane-booking story in plain language for quick clarification. Which GenAI interaction choice fits best?
- A bike-share program needs the same case-generation job after every API specification drop. Which interaction model should the team prefer for that repetitive, well-defined work?
- A credit-union tester claims “the chatbot is magic so we can skip prompt care, and the API tool already has prompts baked in.” What is the best correction?
- A tree-trimming tester walks a chatbot through “list ambiguities, then draft cases” in one sitting, while a colleague’s tool always emits a case table from a ticket id. What contrast does this highlight?
Prompt Engineering for Effective Software Testing · 83 questions
- A county recycling-pickup app needs weekend-overflow test ideas. One prompt begins “act as a test automation engineer,” and another begins “act as a test manager.” Which prompt component sets the persona, tone, and responsibilities for the model?
- A volunteer fire-hall roster story is pasted into a prompt, but overnight mutual-aid and radio-dead zones are never mentioned. Which prompt component supplies the test-object background the model needs to determine relevant test conditions?
- A school-lunch allergy board prompt describes the cafeteria well but never says “list testable acceptance criteria.” Which prompt component is the clear, imperative directive that states the task and its requirements?
- A bicycle-share dock map prompt asks for boundary cases but attaches neither the user story nor the existing case table. Which prompt component is the artefact the model must use — such as user stories, acceptance criteria, screenshots, code, existing cases, or output examples?
- A community-clinic booking prompt says “draft cases” but never notes that after-hours slots are staff-only. Which prompt component captures restrictions or special considerations that say how instructions should be applied to the input?
- A farmers-market stall-lottery tester receives a chatty paragraph when the suite needs a three-column case table. Which prompt component defines the expected format, structure, or characteristics of the response?
- A municipal snow-plow router prompt includes context, instruction, a street list, constraints, and a table format, but no persona. The draft reads like a press release. Which missing component most likely caused the perspective drift?
- A public-pool lane-booking prompt names the tester role, says “write conditions,” pastes the story, limits cases to members, and asks for a bullet list — but never states that the object is a shared outdoor pool with weather closures. Which missing component most likely sent the conditions off-target?
- An animal-shelter adoption prompt includes role, kennel background, the story, a “no live-animal photos in output” limit, and a JSON shape — but never says whether to produce conditions, cases, or data. Which missing component left the task unstated?
- A youth-soccer registration prompt is fully structured except it never attaches the age-band story or last season’s case list. The model invents a generic sign-up form. Which missing component most likely caused the generic testware?
- A library makerspace-booking prompt asks for cases on laser-cutter slot rules but never states “do not invent paid add-ons; minors need a guardian field.” Which missing component most likely caused house limits to be ignored?
- A transit-pass refill kiosk tester wants keyword steps, but the prompt never says “one keyword per line.” The model returns a narrative. Which missing component most likely made the reply unusable downstream?
- A food-coop membership prompt appears as six labeled blocks: “you are a tester,” “this is the produce-share desk,” “draft conditions,” the pasted story, “no medical data,” and “table with id / condition / priority.” Which mapping correctly names all six official prompt-structure components?
- A city-park picnic-permit lead calls the six-part layout (role, context, instruction, input data, constraints, output format) “few-shot prompting.” Which statement best separates prompt structure from prompting techniques?
- A museum audio-guide checkout tester attaches a GUI wireframe and a snippet of the checkout function. A colleague argues those attachments are “context,” not input. Where do screenshots and code belong in the six-component structure?
- A senior-center meal-route tester splits work into “find ambiguities, then rewrite acceptance criteria, then list conditions,” and a person checks each reply before the next ask. Which prompting technique is being used?
- A dog-park day-pass tester pastes three worked story→Gherkin pairs before asking for a fourth. Which prompting technique is this?
- A wastewater lab sample-log tester asks for equivalence-partition conditions from a story and includes no sample row. Which prompting technique is this?
- A community-college waitlist tester includes exactly one house-style case, then a new story. Which prompting technique is this?
- A little-league field-lights tester is unsure how to ask for a risk-ordered condition list and first says “write me the prompt I should use.” Which prompting technique is this?
- A public-housing work-order tester pastes four example cases in one ask. A colleague instead runs “extract rules, then draft cases” as two checked steps. How do these techniques differ?
- A compost-drop weekend tester asks the model to propose a better prompt. Another tester adds two sample defect-report rows to the ask. How do these techniques differ?
- A blood-drive appointment tester first asks the model to draft a prompt, inserts two house examples into that draft, then splits “data then cases” with a check in between. Which statement best describes this approach?
- A town-hall livestream-caption tool is configured once with “you are a software-testing assistant; stay formal; do not speculate.” Every later ask inherits that stance. Which prompt type is this?
- An ice-rink skate-rental tester types a new question each turn, such as “list conditions for the late-fee story.” Which prompt type is this changing, visible ask?
- A community-kitchen reservation chatbot must always act as a shared-kitchen tester and must never invent allergen claims. Where should those standing role, context, and constraint instructions live?
- A boat-ramp launch-permit tester keeps hidden house rules fixed. For this ask they want a case table with id, step, and expected result. Where should that task and output-format instruction go?
- An after-school bus-tracker assistant has a standing system rule to stay ISTQB-aligned and avoid speculation, plus today’s user ask to summarize last night’s failed runs. How does the LLM typically use those prompts?
- A municipal cemetery-plot map chatbot shows testers only a message box. A new hire wants to rewrite the hidden “formal testing assistant” block from that box. What should the candidate recognize?
- A city-bike repair-stand team wants every GenAI session to stay concise and avoid invented part numbers, and they also need one ask that lists boundary values for the torque field. How should those instructions be assigned?
- A public-wifi guest-voucher story says vouchers “expire soon” with no clock, duration, or timezone. Which GenAI analysis ask should the tester choose next?
- A watershed rain-gauge user story is testable but has no condition list yet. Which GenAI prompt goal best fits test analysis?
- A community-radio pledge desk has likelihood and impact notes comparing refund conditions with thank-you-email conditions. Which GenAI analysis ask is most appropriate?
- A county recycling-pickup release has twelve user stories and a backlog of test conditions. Which GenAI analysis ask best supports coverage analysis?
- A school-lunch allergy field is a numeric serving-size with clear minimum and maximum edges. Which GenAI analysis ask fits that condition?
- A bicycle-share dock kiosk provides a user story plus a GUI wireframe. Which GenAI analysis ask is most appropriate for that multimodal basis?
- A community-clinic booking story will be refined across three GenAI asks—ambiguities, then testability, then completeness—with a tester correcting each reply. Which approach should the candidate select?
- A farmers-market stall-lottery tester feeds only the epic title into GenAI and asks for prioritized conditions and coverage. What should the candidate recognize and do?
- A municipal snow-plow story already has test conditions. Which GenAI design ask should the tester choose?
- A community-clinic booking tester needs extreme appointment combinations without using last week’s real patient rows. Which GenAI design ask is appropriate?
- A public-pool lane-booking team has a reviewed case table and needs keyword or framework steps next. Which GenAI ask fits?
- An animal-shelter adoption suite has dependencies (create animal before adopt) and limited evening staff. Which GenAI ask best supports execution planning?
- A youth-soccer registration desk already writes Given–When–Then scenarios. Which prompting approach best emits matching conditions and cases for a new story in that house format?
- A library makerspace-booking tester wants functional cases from acceptance criteria, then a coverage table, then a better prompt for end-to-end procedures. Which approach should they select?
- A transit-pass refill suite was reordered by an LLM using risk plus dependencies. What should the tester do before the night run?
- A food-coop membership GUI already has a keyword library (Login, JoinShare, PayDues). Which GenAI regression ask best fits implementing automated scripts?
- A city-park picnic-permit release changed only the rain-date rule. Which GenAI regression ask best focuses the suite?
- A museum audio-guide checkout button id changed and an API login field was renamed. Which GenAI regression ask addresses that minor drift?
- A senior-center meal-route nightly suite just finished in CI. Which GenAI regression ask is most appropriate next?
- A dog-park day-pass run failed on the late-renewal path. Which GenAI ask best supports enhanced defect reporting?
- A wastewater lab sample-log team maintains two automated suites: one GUI suite that fails when page locators shift after a redesign, and one API suite that fails when the JSON response schema changes. Which GenAI-assisted regression approach best matches each suite?
- A little-league field-lights pipeline runs the full regression suite on every merge, and the suite grows with each sprint. Why is this situation a strong fit for GenAI-assisted creation, maintenance, and optimization of regression tests?
- A community-college waitlist regression log is messy: mixed pass/fail rows, duplicate symptoms, and outdated known issues. Which GenAI prompting approach best supports analyzing that report?
- A public-housing work-order project is slipping exit criteria, and the test manager has fresh monitoring data on progress, defects, and coverage. Which GenAI prompt best supports test monitoring?
- A compost-drop weekend run is behind schedule, and the weigh-station story just grew in scope. Which GenAI prompt best supports test control?
- A blood-drive appointment release is closing, and leadership wants a clear test-completion view for the next cycle. Which GenAI use best fits?
- A town-hall livestream-caption test manager needs non-testers to understand progress before tonight’s broadcast. Which GenAI prompt best helps?
- An ice-rink skate-rental team already stores comments, logs, and counts in their test-management tool. They want GenAI help with monitoring. What should the monitoring prompt primarily do?
- A community-kitchen reservation lead asks, “Are we still on the quality plan?” Which GenAI ask is the best fit?
- A boat-ramp launch-permit dashboard shows coverage flat while severity-1 defects are rising. What should the team’s next GenAI ask prioritize?
- A municipal cemetery-plot map analysis must move from ambiguity findings to testability notes to completeness checks, with a person signing off after each hop. Which prompting technique should the tester select?
- A city-bike repair-stand team must emit the same keyword-driven script shape about fifty times for similar repair flows. Which prompting technique is the best primary fit?
- An after-school bus-tracker tester has never asked an LLM to cluster anomalies in a messy completion log and does not know how to phrase a good prompt. Which technique should they select first?
- A public-wifi guest-voucher tester lets the model propose a prompt, adds two house examples to lock the pattern, then splits extract-rules / draft-cases / check-coverage with a review between hops. Which approach does this illustrate?
- A watershed rain-gauge team has no accepted sample of a flood-alert condition list and does not want to invent fake examples that would mis-train the format. Which technique should they select?
- A community-radio pledge desk has exactly one blessed defect-report template and a new failure to describe. Which prompting technique is the best fit?
- On one morning a park-permit team faces three asks: (1) risk-ordered conditions with a check after each factor, (2) twenty Gherkin twins in the house style, and (3) a completion narrative they have never prompted before. Which technique mapping is correct?
- A food-coop membership tester uses few-shot prompts filled with grocery-website examples to perform a first-time, multi-step coverage analysis of a produce-share rule. What is the best assessment of that choice?
- A county recycling-pickup tester compares GenAI-generated cases to an expert-written reference set and the specified requirements. Which quality metric is the tester primarily assessing?
- A volunteer fire-hall roster tool uses GenAI to flag “anomalies” in last night’s run, but many flags are not real defects. Which quality metric is most directly suffering?
- A school-lunch allergy data class has both valid and invalid partitions, but a GenAI-generated pack covers only the valid band and misses the invalid one. Which metric best names this gap?
- A bicycle-share dock model writes polished test cases for a paid highway transponder program the city does not operate. Which quality metric is most clearly failing?
- A community-clinic booking pack repeats the happy path eight times and never books the last slot of the day. Which quality metric is most clearly weak?
- A farmers-market stall-lottery script pack looks neat in review, yet half the scripts fail to start because of syntax or output-format errors. Which metric names this problem?
- A municipal snow-plow lead wants a single stopwatch reading from one lucky GenAI generation to prove time savings. What is the soundest response regarding metrics?
- A public-pool lane-booking tester starts with a thin prompt, gets a weak case pack, then adds the member/guest glossary and tightens the verbs before regenerating. Which refinement practice is this?
- An animal-shelter adoption pair writes two prompt versions—one asking for a table, one for numbered steps—and scores both on the same predefined metrics. Which practice are they using?
- A youth-soccer registration pack invents a waitlist the user story never granted. The tester catalogs that miss and rewrites the prompt to stay inside the story. Which refinement practice is illustrated?
- Library makerspace testers say generated procedures skip tool-safety checks they always perform, so the prompt is updated to require that detail level. Which refinement practice is this?
- A transit-pass refill prompt grew into a page of background and the cases became generic; a shorter, sharper version recovered the kiosk edge cases. Which refinement practice does this illustrate?
- A food-coop membership squad holds a weekly prompt-evaluation hour and stores winning prompts in a shared library so the next tester avoids last month’s misses. Which practice is this?
- A city-park picnic-permit pack scores well on execution success rate but poorly on diversity. What should the tester’s next refinement prioritize?
- A museum audio-guide tester runs one prompt, likes the tone, and wants to freeze it without comparison or further review. What should happen instead?
Managing Risks of Generative AI in Software Testing · 75 questions
- A recycling-center scale-house tester asks an LLM for cases covering posted surcharge rules. The reply confidently includes a full pack for a “hazardous-oil surcharge” that never appears in the posted rules. Which GenAI defect does this best illustrate?
- A municipal water-meter portal draft from an LLM lists an acceptance criterion that “a renter may reset a landlord’s leak alert.” That rule is nowhere in the user story. Which defect is this?
- A county poll-book checkout script from an LLM looks tidy but calls screens and APIs the election app does not expose, so it will not run. Which GenAI defect does this represent?
- A community-college registration planner receives this from an LLM: “If the waitlist is full, night labs close; night labs are closed; therefore the waitlist is full.” Which defect type is this?
- A city cemetery-plot system asks an LLM to sequence staff effort. The reply treats “engraving takes two days” as a reason to skip the legal-hold check that must run first. Which defect is shown?
- A municipal dog-license desk is told the LLM “thinks like a clerk” about late-fee ladders. Why do math-like and multi-step test tasks often go wrong?
- A public-beach locker kiosk prompt asks an LLM for guest names for test data. The reply returns only English-language given names even though the city serves several language communities. Which defect does this illustrate?
- A fire-station equipment-checkout pack from an LLM (1) invents a hose not in inventory, (2) issues a radio before the required drill sign-in, and (3) writes locker IDs only in one alphabet. Which mapping is correct?
- A volunteer-fire shift app generates a case that “overtime starts after six hours,” but the posted SOP says eight. The tester compares the pack to the SOP and flags the extra rule. What detection approach is this?
- A municipal compost-pickup pack invents a “holiday skip if snow exceeds two inches” rule that looks plausible to a new tester. The solid-waste supervisor says that rule was never adopted. Which detection method does this exemplify?
- In one generated senior-center meal-delivery pack, one case says the cutoff is 10:00 and another says 11:30. Both sound locally plausible. What should the tester conclude and do?
- A city-zoo membership script from an LLM says: open join page → tap Pay → then enter card → then tap Submit, attempting payment before a card exists. How should the tester classify and detect this?
- A public-radio pledge script is syntactically tidy; when run against the pledge form it clicks a control that is not on the page. Which detection approach revealed the defect?
- A municipal court-scheduling strategy requires weekday, evening, and interpreter-assisted bookings, but the LLM’s synthetic data is weekday-English only. How should the tester identify this problem?
- A county 311 intake pack from an LLM is all happy-path functional clicks; load, accessibility, and privacy cases never appear even though the charter lists them. What defect does this show?
- A public-school bus-routing pack will feed a safety-critical stop list. A colleague wants the same light skim used on a cafeteria-menu wording draft. What should the tester select?
- A community-garden plot lottery LLM keeps inventing a “senior override” because the prompt omitted the posted eligibility table. Adding that table as input data stops the invention. Which mitigation is this?
- A municipal ice-rink booking pack botches a three-rule fee ladder in one shot. The tester splits the work into “list rules → apply one booking → check the total” and reviews each reply before the next. Which mitigation is named?
- A community-band rehearsal-hall brief arrives as a scanned poster with overlapping notes; the LLM mixes call times. The tester re-supplies the same facts as a simple table. Which mitigation does this illustrate?
- A municipal wedding-license desk uses a general chat model for a multi-step waiting-period calculation and keeps getting the order wrong. Switching to a model suited to that logical task is proposed. Which Chapter 3 mitigation is this?
- A county flood-alert pack from one model invents a “text all residents at 3 a.m.” acceptance criterion; a second model stays inside the posted policy. The tester compares both replies before promoting any case. Which mitigation is this?
- A municipal lost-and-found chatbot is prompted only with “write tests” and no desk rules. The pack invents shelves and reverses claim order. What does this situation illustrate about defect likelihood?
- A community-tool-library lead wants this chapter’s risk item to teach embedding stores and a tuning job so invented loan rules stop. What is the correct Chapter 3 stance?
- A municipal bulk-trash scheduler sends the same prompt twice and receives two different pickup-route cases. What causes this non-deterministic behavior?
- A county animal-control script pack changes verbs on every run. The tester is advised to reduce randomness so wording stays more consistent. Which action matches that advice?
- A municipal after-school sign-out generator becomes repetitive once the team lowers temperature for more stable wording. What trade-off does a low temperature setting introduce?
- A city taxi-medallion desk needs stable automation locators from an LLM, not brainstormed edge-case names. Which temperature choice best fits that reproducibility-first task?
- A community-kitchen rental tester needs yesterday’s GenAI data pack again to compare two prompt versions fairly. Which control reuses the same pseudo-random sampling sequence in implementations that support it?
- A sidewalk-cafe-permit lead claims that setting a random seed “turns the LLM into a deterministic rule engine.” What does a seed actually provide?
- A county deed-recording pair sees two GenAI runs diverge and debates whether to change temperature or set a seed. How do these two mitigations differ?
- A public-art mural-permit pack still drifts on long scripts even after the team uses a low temperature and a seed. What should the team remember about reproducibility and verification?
- A snow-emergency parking tester pastes a live tow list with names and plates into a chatbot; a later summary repeats a plate that should never have left the yard. Which privacy risk does this illustrate?
- A community-choir ticket desk learns the chatbot vendor keeps prompts to “improve the model,” and the desk cannot say who sees last night’s donor list. Which privacy concern does this describe?
- A municipal birth-certificate counter wants a public chatbot to draft cases from real applicant rows that include names, dates of birth, and addresses. What compliance risk should the tester raise?
- A county jury-summons tool embeds an LLM beside the juror file used in test workflows. Beyond ordinary application bugs, what security risk should testers recognize?
- A public-boat-ramp kiosk team is briefed that someone might try to alter the test assistant’s behavior or pull sensitive slip-holder data through crafted input. Which risk class does this briefing describe?
- A municipal parking-boot desk is warned that planted files in the test-data drop could push the assistant into wrong conclusions about who is booted. Which risk does this warning name?
- A city bus-transfer tester sorts two briefings: (1) yesterday’s prompt still sits on a vendor disk, and (2) an outsider might reach the test-tool account. How should these concerns be mapped?
- A community-orchard harvest kiosk security brief asks testers to name the four official GenAI security vectors in syllabus v1.1. Which set is correct?
- A fire-hydrant-use-permit team is told that context manipulation targets confidential training data, for example by overloading the context window until stray training snippets appear. What is the correct tester stance?
- A county septic-inspection study sheet still labels a §3.2.2 row as “data exfiltration.” Which v1.1 name should the candidate select for that row on the exam?
- A dark-sky-park reservation assistant starts emitting fragments that look like another city’s logs, not the posted park rules. What is the appropriate tester response to this possible context-manipulation leakage symptom?
- A municipal leaf-collection brief defines a GenAI security vector as introducing data that disrupts the AI’s output — for example, an image that lures the model into another context and provokes hallucinated acceptance criteria. Which vector is that?
- A city food-truck-permit tester attaches a vendor sketch; the generated acceptance criteria include “skip health-sticker if the truck is electric,” which is not in the ordinance. Security later says the sketch was tainted input. What should the tester conclude?
- A workshop kiln-booking lead hears that people may submit fake evaluations when rating an AI-generated test report. Which official security vector names manipulating training or rating data this way?
- A municipal storm-drain report assistant keeps marking “blocked inlet on a school walk” as low severity after a week of unexplained five-star ratings on sloppy drafts. What should the tester do?
- A public-observatory booking generator is supposed to emit a UI script. Review finds an unexpected outbound “diagnostics” channel that nobody requested. Which security vector has the reviewer likely found?
- A municipal yard-waste tester wants to execute a freshly generated keyword script in the shared garage because it compiled cleanly. Given malicious code generation as a named risk, what is the correct stance?
- A city crossing-guard scheduler is given two risk cards: (A) the model may leak training snippets after its window is abused; (B) a planted image may push invented acceptance criteria. How should the cards be mapped?
- A community-beehive-registration pair must label two events: (1) someone stuffed fake “looks good” scores on generated inspection reports; (2) a generated hive-sensor script grew an unexpected remote call. How should these events be mapped?
- A municipal dump-sticker designer pastes the city’s unpublished fee table and a vendor’s unreleased locator map into a public chatbot to draft test cases. Which privacy or security concern does this scenario best illustrate?
- A county plat-map desk learns that someone is submitting bogus star-ratings on the AI-written weekly test report to skew what the team trusts. Which named vulnerability does this scenario match?
- A public-sauna reservation tester is handed a huge untrusted paste plus a mystery PNG and told to “just generate the pack” with an LLM. What is the most appropriate tester gate?
- A municipal pothole-report tester is about to paste a full citizen export—phones and emails included—into an LLM just to draft three cases. Which mitigation principle should guide the prompt?
- A city tree-removal permit desk replaces real owner names and parcel IDs with tokens before asking an LLM for boundary-related test data. Which mitigation strategy is being applied?
- A community-darkroom booking log with member IDs will travel to an LLM-powered test tool. Which mitigation best addresses protection of that data in motion and at rest?
- A municipal parking-garage overnight crew keeps pasting gate-camera stills into a public chatbot because “nobody said not to.” Which organizational mitigation directly addresses this pattern?
- A county health-inspection pack includes a generated script and a severity table from an LLM. Which mitigation is essential before the pack is used?
- A public-archive retrieval team handles sealed personnel files and wants stronger GenAI privacy and security controls for test tasks. Which pair of complementary mitigations best fits the syllabus guidance?
- A municipal tree-planting-permit program will keep an LLM in the test toolchain for years. Which set of ongoing controls best matches the syllabus mitigations?
- A municipal ice-cream-cart permit desk compares a one-line text rewrite with a long multi-file analysis in an LLM-powered test tool. What primarily drives the difference in energy consumption?
- A municipal ski-tow ticket team wants a model to paint a fake lodge UI for every case instead of only writing the steps in text. Which energy comparison from the syllabus best applies?
- A county library-of-things tester shrugs that “one more regenerate” is nothing, then the squad does it on every case every sprint. Which environmental point does this illustrate?
- A public-pier fishing-license kiosk regenerates the same happy-path pack six times “for luck” with no prompt change. Which official environmental practice should the tester apply?
- A municipal campground lead refuses to care about GenAI energy use until given an exact kilogram of CO₂ per test case. Which syllabus stance best responds?
- A city fountain-show booking chatbot is used all afternoon from testers’ laptops for GenAI-assisted case drafting. Where does that usage increase load and energy use?
- A municipal greenhouse-plot tester can either attach twenty decorative site photos or paste the three posted rules as text when asking an LLM for cases. Which environmental choice is better when the text is sufficient?
- A county weigh-station draft item begins: “Open the accredited-course energy simulator and enter token counts.” Why should that stem be rejected for the written CT-GenAI bank?
- A city hall-rental test lead asks which named instrument specifies requirements for managing AI systems in the organization and promotes consistent, reliable GenAI-in-testing practice. Which answer is correct?
- A municipal boat-slip waitlist team wants the named framework for AI systems using machine learning that stresses lifecycle, data quality, transparency, and safety for GenAI used in testing. Which instrument is that?
- A sidewalk-snow-shovel registry will use GenAI on a high-visibility city service. Which named instrument is the regulation that classifies applications by risk level and mandates transparency, accountability, and bias mitigation for GenAI used in testing?
- A county 4-H fair-entry desk wants the named framework—not a statute—that offers guidelines for managing AI risks with a focus on fairness, transparency, and security, and that supports preventing biased test results. Which instrument is it?
- A municipal outdoor-movie-permit board shows four cards: ISO/IEC 42001:2023, ISO/IEC 23053:2022, the EU AI Act, and NIST AI RMF 1.0. Which labeling of instrument types is correct?
- A homestead-exemption desk must match four GenAI-in-testing needs: (1) manage AI systems for consistent practice; (2) ML lifecycle, data quality, and safety; (3) legal risk classes plus accountability and bias duties; (4) fairness, transparency, and security guidelines that help avoid biased test results. Which mapping is correct?
- A county clerk wants a bank item to quote EU AI Act article numbers and every NIST function in detail. What should the exam writer recall about scope?
LLM-Powered Test Infrastructure for Software Testing · 37 questions
- A community-garden plot desk needs a system that reads this week’s watering rules and emits a case table, not a chat pane that only answers “what is a test condition?” Which description best fits an LLM-powered test tool or test infrastructure?
- Municipal compost-pickup testers type a ticket id and a “draft weekend cases” command into a form before the suite does anything else. Which architecture component is that form?
- A county-fair livestock-registration suite must log who asked, fetch last year’s weigh-in cases, wrap them in a house prompt, and only then call the model. Which component typically handles authentication, data retrieval, prompt preparation, and interaction with the LLM?
- A public-pool lane-reservation shop can either send structured prompts to a vendor model over an API or host a custom model on its own servers. How may the LLM component be provided in test infrastructure?
- A community-orchestra ticket-exchange lead calls the new suite “just a web form talking to a file server.” Why is that description incomplete for an LLM-powered test infrastructure?
- A municipal dog-park-pass suite stores approved cases in rows and columns and wants the back-end to pull those rows when it builds a prompt. Which store is appropriate for that structured testing data?
- A community-kiln booking handbook is stored as meaning vectors so “glaze hold time” can surface a nearby “cooling schedule” note. Which store supports that semantic retrieval?
- A municipal boat-ramp reservation model returns a raw case list that still mentions a retired “cash-only” rule. The suite strips or flags that line before testers see the pack. What is that back-end step called?
- A county mosquito-spray notice shop finds the plain model invents last year’s exclusion streets and wants cases grounded in the current spray handbook. What does RAG primarily add?
- A paratransit-booking policy binder will not fit in one prompt, so the shop breaks it into pieces of about 256–512 tokens. Why is that chunking done?
- A community-fridge leftover-share handbook is tidied, turned into high-dimensional vectors with a pre-trained embedder, and saved for later lookup. Which sequence best summarizes that preprocess path?
- A municipal yard-waste bag-tag tester asks for cases about “late-set-out on a holiday.” The suite encodes that ask, pulls nearby handbook chunks, and only then writes cases. Which pair names those runtime steps?
- A volunteer fire-hall equipment-checkout lead asks what “relevant” or “grounded” means after RAG runs. Which summary is best?
- Municipal ice-rink slot rules change every freeze warning, and testers need analysis and design to follow this week’s handbook and the existing case store. How does RAG support that testing need?
- A community-darkroom booking lead wants every page of the chemical-safety binder stuffed into the prompt “so nothing is missed.” What is the better description of how RAG works?
- A municipal rain-barrel rebate instructor wants a written exam item that is “open the tool, run with RAG and without, and score the two packs.” What should a candidate recognize about that request?
- A community seed-library suite can open the case tracker and attach a draft pack after a tester states a goal. How does an LLM-powered agent differ from a chatbot that only answers?
- A county septic-pump scheduler lets an agent refresh overnight regression notes with almost no one watching. Which autonomy label fits that pattern?
- A community beehive-registration desk lets an agent draft inspection cases but a tester signs each pack before it is stored. Which autonomy pattern is that?
- A municipal splash-pad suite uses one agent to pull stories, one to draft cases, and one to write the daily report, with a coordinator passing work between them. What architecture pattern is this?
- Testers at a community-radio playlist desk see an in-tool helper that turns a new “no-overlap weekday” story into analysis notes, cases, and a short report without leaving the test application. What are AI assistants in this context?
- A municipal sidewalk-cafe permit shop used to maintain brittle click-scripts. The new suite is told “confirm a weekend-patio application can be refused for missing insurance” and works toward that goal. What shift does that illustrate?
- A community-woodshop reservation agent books a “dust-gate closed” check that the shop never required. What risk does this illustrate for agents?
- Municipal leaf-collection routing is safety-visible: a wrong skip-day case could strand a street. Which mitigations does the syllabus name for agent risk on such work?
- A community quilt-guild booth shop has a generic model that writes cases in a public textbook style and wants it to learn guild wording after a further training pass on labeled house examples. What is fine-tuning here?
- A municipal fishing-pier pass team wants every generated case in their three-column house sheet and will train on last season’s stories paired with accepted cases. What is the syllabus testing example of fine-tuning?
- A community makerspace laser-booking desk cannot host a large general model but can further train a smaller one on its safety-card cases. Why might the shop fine-tune an SLM?
- A municipal bus-stop snow-clear pack used last year’s cases that almost never included curb-cut routes; after fine-tuning, new packs still skip those streets. Which fine-tuning challenge does this show?
- A community orchard-gleaning model, after a tuning pass, only writes cases that copy last October’s three stories and fails on this week’s new “wet-ladder” rule. Which challenge is that?
- A municipal kayak-rack rental lead cannot tell why the tuned model added a “life-jacket color” check. Which fine-tuning challenge is named by that opacity?
- A community little-free-library instructor wants the bank item to walk through selecting a framework, loading a dataset, and launching a training job. What should candidates recognize?
- A municipal street-piano booking shop asks what discipline covers deploying, monitoring, and maintaining the model that drafts weekend cases once it is in daily use. Which term fits?
- A community dark-sky event desk will let testers chat with a model about story wording. Which LLMOps concerns are highlighted on that chatbot path?
- A municipal bee-friendly lawn-waiver shop will buy a test tool that already embeds GenAI for case drafts. Beyond shared privacy, security, and cost concerns, what else should that path evaluate?
- A community climbing-wall waiver team wants to build its own LLM-powered case helper so wall-height rules never leave the building. What does the in-house GenAI test-tool path emphasize?
- A municipal ice-fishing shelter-tag shop chats about new stories, uses a purchased GenAI test tool for nightly case refresh, and is prototyping one in-house helper for tag-color rules. What does that combination illustrate about LLMOps approaches?
- A community sauna-booking lead hears “once LLMOps is in, the model runs itself and we can skip review,” and separately that RAG or a house fine-tune cannot sit under the same operating plan. Which statement is accurate?
Deploying and Integrating Generative AI in Test Organizations · 52 questions
- A municipal dog-license tester pastes tonight’s defect notes into a personal chatbot on a home laptop because the shop has not approved any GenAI tool. What term describes using GenAI without formal approval or oversight?
- A community grain-mill booking tester feeds resident names from a failed reservation into an unsanctioned personal GenAI helper. Which shadow-AI risk does this primarily illustrate?
- A municipal rain-garden inspection pack is drafted in a personal GenAI helper even though the city requires AI processing only under approved instruments. Which shadow-AI risk is illustrated?
- A community mural-permit tester drops an artist’s unpublished sketch notes into a free personal model whose license terms are unclear. Which shadow-AI risk does this primarily illustrate?
- A municipal street-fair stall desk sees testers “just using whatever GenAI works” and wants to stop that pattern. According to the syllabus, what helps a test organization avoid shadow AI?
- After hours, a community book-bike tester pastes a live checkout outage into an unapproved chat pane “just to draft a defect report faster.” How should this situation be classified?
- A municipal food-scraps drop shop already has an approved in-tool assistant, but a tester uses a look-alike personal account with no logging. What makes the personal-account use shadow AI?
- A community apiary-inspection lead asks whether shadow AI is the same as context manipulation, request manipulation, data poisoning, and malicious generated code. How should those ideas be distinguished?
- A municipal ice-rescue volunteer roster shop writes “use AI more” as its only GenAI testing goal. Which set best represents measurable test objectives for a GenAI testing strategy?
- A community bread-oven reservation desk picks a huge general model that will not run on the current test server and cannot grow with Saturday rush. What strategy aspect is missing in that LLM choice?
- A municipal pier-fishing license team plans to feed whatever inbox mail arrives into their LLM-powered testing helper. Why does the GenAI testing strategy call out input-data practices?
- A community print-shop booking manager budgets only for a model license and assumes testers will “pick it up.” Which GenAI testing strategy pillar is missing?
- A municipal alley-light outage shop has GenAI testing objectives but no plan to score generated packs. What should the strategy include regarding effectiveness?
- A community loom-reservation strategy is silent on whether resident phone numbers may be pasted into prompts. What process guideline belongs in the GenAI testing strategy?
- A municipal sidewalk-repair ticket pack is filed with no mark that an LLM drafted the steps. Which strategy guideline obligation is missing?
- A community cider-press booking lead wants generated cases to go straight into the nightly run with no stop. Which process guideline belongs in the GenAI testing strategy?
- A municipal boat-mooring waitlist shop wants to pick a model using only a public code-generation leaderboard. What should the team remember about benchmarks for software testing?
- A community film-club booking team cares whether a model drafts usable weekend cases, not whether it wins a general language contest. Which LLM selection criterion does that priority reflect?
- A municipal snow-fence request shop may later train a model on last year’s fence-map cases so wording matches house sheets. Which selection criterion does that future option represent?
- A community dye-vat reservation desk compares a commercial hosted model with an open-source-licensed model they would operate themselves. Which selection criterion focuses on ongoing spend?
- A municipal hydrant-flush notice team has no in-house LLM specialist and needs docs plus an active user community when their case helper misbehaves. Which selection criterion matches that need?
- A community clay-pit booking shop must handle a photo of the pit board plus a long safety note, and legal has already ruled out one license type. How should those model differences inform selection?
- A municipal trail-groomer instructor wants a written exam item that opens vendor price lists and computes a monthly token bill. What is the appropriate exam depth for LLM selection?
- A community smokehouse-reservation board asks for the official sequence a test organization uses when adopting GenAI. Which ordered list is correct?
- A municipal bulk-water station has not picked a lasting use case yet; testers are taking a short GenAI class and trying a sandbox model on a dummy story. Which adoption phase is this?
- After sandbox experiments, a community letterpress shop ranks “draft cases from stories” above “auto-write the annual report” and starts judging which infrastructure would fit. Which adoption phase is this?
- A municipal wharf-crane booking desk now runs GenAI inside daily test work, watches progress, and adjusts so the benefit lasts. Which adoption phase does this describe?
- At a municipal marina office, GenAI-assisted defect-report analysis is already part of daily testing while automated UI scripting is still in first sandbox trials. What does this illustrate about GenAI adoption phases?
- Testers at a community worm-bin booking desk stall a GenAI pilot because they believe the assistant will replace them. Which adoption concern should the shop recognize and address early?
- A municipal spray-park test team has completed GenAI training and opened a sandbox, but has not yet ranked lasting use cases for daily work. In which adoption phase is this shop?
- A community ham-shack test lead wants “write the roadmap” listed as official phase four of GenAI adoption. Which statement is correct?
- A municipal chipper-day tester knows CTFL techniques well but pastes an entire season log that will not fit the model and then writes only “test this.” Which skill areas does this highlight as essential for GenAI-assisted testing?
- A community spinning-wheel booking pack is accepted because it “sounds like a tester wrote it.” What essential GenAI testing skill does this shop still need?
- A municipal eel-trap permit shop hires only “prompt people” who lack fishing-rule and testing knowledge to judge generated cases and defect analyses. What expertise combination do testers need?
- A community CNC-router booking tester treats every GenAI-generated script as safe to run without further thought. Which essential knowledge area is missing?
- A municipal bus-shelter-ad tester is about to paste a complainant’s home address into a prompt to an LLM-powered test tool. Which skills should stop that action?
- A community u-pick orchard desk uses a giant multimodal model to rewrite a one-line reminder case. Which tester skill does right practice emphasize?
- A municipal canoe-lock instructor wants a Chapter 5 skills item that asks candidates to pick few-shot versus chaining for a given story. Why is that the wrong focus for GenAI-5.2.1?
- A community storywalk team is given only a login and told to “figure GenAI out.” Which cultivation strategy does official practice recommend instead?
- One municipal pop-up-stage tester already writes solid GenAI prompts, but the rest of the desk has never seen those examples. Which cultivation strategy addresses this gap?
- A community meteor-watch shop wants a one-week “AI takeover” of every test activity. Which cultivation strategy should guide them instead?
- New testers at a municipal no-mow-May desk still write only “make tests” after the intro class. What official skill-progression line should cultivation follow?
- A community bouldering-wall shop keeps rewriting the same “draft cases from this story into our three-column sheet” text. What is a prompt pattern in this context?
- Testers from several municipal warming-hut projects never meet to compare which GenAI-for-test prompts worked. Which cultivation practices address this?
- A community steam-room booking success with GenAI-assisted testing is lost when that tester leaves. What cultivation practice reduces that risk?
- A municipal cat-license tester believes adopting GenAI means stopping test techniques and only clicking “generate.” How does the tester role actually shift?
- A community flour-share desk asks what new daily work testers take on when GenAI enters testing. Which expanded task set is recognized?
- A municipal bioswale inspection manager leaves “the AI plan” to whoever installed the chatbot. Which test-manager responsibility shift does this miss?
- A community banner-hang shop has a GenAI strategy slide, but nobody watches whether GenAI packs are safe or on plan. Which manager responsibilities are missing?
- A municipal night-market lead wants to drop equivalence-partitioning class because “the model covers it.” What should managers recognize instead?
- A community bookmobile manager still plans only human assignments even though an in-tool agent now drafts the nightly pack. What managerial shift should they recognize?
- A municipal maple-tap permit instructor wants a Chapter 5 role-shift item that asks how to test the assistant as the product under test. Why is that out of scope for this bank?
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