Search the practice set
275 questions, 991 terms and 600 topics in 30 areas.
60 results for “Quality Gates”
Definition of Done
The agreed, explicit conditions under which work is finished, which functions as a quality gate only when it is automated and non-negotiable.
Quality Attribute Scenario
A structured, testable statement of a non-functional requirement: source, stimulus, environment, artefact, response, response measure.
Quality Dimension Threshold
The stated numeric level at which a dataset is fit for its purpose on a given quality dimension, plus what happens when it is not met.
Runbook Quality
The properties that make an operational procedure usable by a tired responder under pressure, as opposed to a document that merely exists.
Alert Actionability
The proportion of alerts that result in a human taking action, used as the primary quality measure of an alerting system.
Architecture Trade-off Analysis Method
A structured evaluation that scores an architecture against prioritised quality-attribute scenarios and identifies the points where those attributes conflict.
Automated Release Verification
A gate that compares the new version's live signals against the old one's and decides, on stated criteria, whether to continue or revert.
Brownout
Deliberately reducing the quality or completeness of every response under load, rather than serving some requests fully and rejecting others.
Cross-Functional Requirement
A quality the system must exhibit across its features rather than a behaviour it must perform, so named because it cuts across all functionality.
Data Catalog
A searchable inventory of datasets with their schema, owner, meaning, freshness, quality and classification.
Data Contract
An explicit, versioned, enforced agreement between a data producer and its consumers about schema, semantics, quality and change policy.
Delivery vs Maintainability
Choosing where to take deliberate shortcuts, based on which kinds of debt are cheap to repay and which compound.
Evidence Based Approval
Replacing a human judgement about whether a change is safe with a machine-produced record of the checks it passed, assessed once for the class rather than per instance.
Inference Telemetry
Recording the full context of each model interaction — inputs, outputs, tokens, latency, model version and evaluation scores — so quality and cost can be investigated.
LLM Evaluation
A repeatable measurement of whether an AI system's outputs are good enough, on cases that reflect the actual task.
Non-Functional Acceptance
Treating quality-attribute targets as acceptance criteria with automated verification, so a release can fail on latency the way it fails on a broken feature.
Non-Functional Requirement
A requirement about how well the system must behave rather than what it must do — latency, availability, throughput, security, cost.
Portfolio Assessment Matrix
Plotting each application on business value against technical quality, producing a disposition for every system in the estate.
Preventive Control Placement
Choosing where in the lifecycle a control acts — at authoring, at admission or after the fact — which determines both its strength and its cost.
Prompt Regression Suite
A set of test cases with expected properties, run against a prompt on every change, to detect quality regressions before deployment.
Test Pyramid
A distribution of tests weighted towards many fast unit tests, fewer integration tests, and very few slow end-to-end tests.
Test Strategy Altitude
Deciding which risks are verified at which level, so that each layer tests something the layers below it structurally cannot.
A client wants an assistant that answers questions from 50,000 internal documents which change weekly. RAG or fine-tuning? What actually determines the quality?
What the interviewer is testing Whether you understand what each technique actually does, and whether you know that RAG quality is a retrieval problem. Why RAG
You are setting data quality thresholds for a dataset with five consuming teams. How do you decide the numbers?
Not by the data team, and not one number for everyone Fitness for purpose differs by consumer. Ninety five percent completeness on an attribute is fine for a ma
A project is three sprints from launch and no non-functional requirements were ever written down. What do you do?
Do not write a full NFR matrix now There is no time, and most of the design decisions those numbers would have constrained have already been made. Writing twelv
A quarterly board report shows a category down 40%. Investigation finds an upstream system stopped sending a field three months ago. Nothing alerted. What do you change?
Understand why nothing fired The pipeline completed successfully every night. It read the source, applied its transformation, and wrote rows — all of which is w
Quality Gates
Thresholds that block a release, who may override them, and how they decay.
Change Advisory vs Automated Gates
Replacing a weekly board with evidence a machine produces on every change.
Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
Deployment Gates
Automated verification between stages, and the difference between a gate and a delay.
Guardrails vs Gates
Preventing a class of mistake automatically versus stopping to ask a human.
Quality Attributes
Availability, latency, throughput, security, cost — expressed as testable scenarios.
Testing & Quality Architecture
General material on designing a testing strategy as an architectural concern.
Accessibility Testing
Automated checks, their ceiling, and the manual testing that has to sit above it.
Chaos as a Test
Fault injection with a hypothesis, a blast radius and an abort condition.
Chunking & Retrieval
Structure-aware splitting, hybrid search and why chunking dominates quality.
Consumer-Driven Contracts
Consumers declaring what they rely on, and providers verifying against those declarations.
Contract Testing at Scale
Keeping dozens of services compatible without an environment that runs all of them.
Data Governance
Ownership, lineage, quality, catalogues and who may see what.
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
End-to-End Test Economics
Why broad end-to-end suites get slow, flaky and abandoned, and what to keep.
Environment Parity
The differences between staging and production that decide which bugs survive to release.
Flaky Test Management
Quarantine, detection, and the trust a suite loses once red stops meaning broken.
Functional vs Non-Functional
Behaviour versus quality of behaviour, and why only the second constrains structure.
Integration Test Boundaries
What sits inside a test's boundary, what is faked, and the confidence that follows.
LLM Evaluation
Held-out sets, rubric judging, CI gates and production sampling.
Mutation Testing
Measuring whether tests would actually notice a defect, not just cover a line.
Non-Functional Test Strategy
Testing availability, latency, security and recovery rather than only behaviour.
Performance Test Design
Workload models, warm-up, think time, and the distribution the average hides.
Security Testing in the Pipeline
SAST, DAST, dependency and secret scanning, and what to do with the findings.
Service Virtualisation
Standing in for a dependency you cannot call, and keeping the stand-in honest.
Synthetic Data
Generating data with the shape and edge cases of the real thing, and where it misleads.
Test Architecture Strategy
Choosing what to verify where, given the failure modes that actually occur.
Test Data Management
Realistic data without copying production personal data into a weaker environment.
Test Pyramid Shapes
Pyramid, trophy and honeycomb, and the system properties that justify each shape.
Testing in Production
Synthetic transactions, dark launches and shadow traffic, done deliberately and safely.